Adaptive Bitrate Video Streaming for Wireless Nodes: a Survey Kamran Nishat,Omprakash Gnawali, Senior Member, IEEE and Ahmed Abdelhadi, Senior Member, IEEE

Adaptive Bitrate Video Streaming for Wireless nodes: A Survey Kamran Nishat,Omprakash Gnawali, Senior Member, IEEE and Ahmed Abdelhadi, Senior Member, IEEE

Abstract—In today’s Internet, video is the during video streaming to sustain revenues [3] most dominant application and in addition to and user engagement [4]. Most Internet video this, wireless networks such as WiFi, Cellu- delivery services like Twitch, Vimeo, Youtube lar, and Bluetooth have become ubiquitous. Hence, most of the Internet traffic is video use Adaptive BitRate (ABR) to deliver high- over wireless nodes. There is a plethora of re- quality video across diverse network conditions. search to improve video streaming to achieve Many different types of ABR are implemented high Quality of Experience (QoE) over the In- in recent years [5, 6] to optimize the quality of ternet. Many of them focus on wireless nodes. the video based on different inputs like available Recent measurement studies often show QoE of video suffers in many wireless clients over bandwidth and delay. Recently, Pensieve, a neu- the Internet. Recently, many research papers ral adaptive video streaming platform developed have presented models and schemes to op- by MIT [5], it uses deep reinforcement learning timize the Adaptive BitRate (ABR) based (DRL) [7, 8], and outperforms existing ABRs. video streaming for wireless and mobile users. One of the major challenges will occur in In this survey, we present a comprehensive overview of recent work in the area of Internet the near future with 5G wide deployment when video specially designed for wireless network. many devices share the unlicensed spectrum, Recent research has suggested that there are such as [9–11] .Video stream applications can some new challenges added by the connec- be optimized for these resource critical scenar- tivity of clients through wireless. Also these ios with the introduction of edge device based challenges become more difficult to handle when these nodes are mobile. This survey feedback to the Reinforcement Learning (RL) also discusses new potential areas of future based ABR running on the server. These edge research due to the increasing scarcity of wire- devices will collect data by spectrum sensing and less spectrum. then allocate the spectrum for the next time Index Terms—Video, Wireless, WiFi, Cel- slot. This allocation will be transmitted to the lular, Spectrum Sharing. ABR server in addition to the feedback from the mobile client. I. Introduction A. Motivation: Why we need ABR solutions for IDEO is the most frequent type of traffic arXiv:2008.00087v1 [cs.NI] 27 Jul 2020 Wireless Networks? V on today’s Internet [1, 2]. It is important for services like Youtube, Netflix, and Facebook Video streaming over wireless/mobile nodes to deliver a high Quality of Experience (QoE) now accounts for more than 70% of Internet traffic, and it is still growing with a phenomenal Kamran Nishat, Omprakash Gnawali and Ahmed rate [1]. Massive deployments of LTE based Abdelhadi are with the University of Houston, cellular networks has also played a vital role in Houston, TX 77004, USA (e-mail:[email protected], [email protected], [email protected]) this. LTE supports peak down-link bitrate of 300 Manuscript received XXX Mbps, almost 10 times more than over 3G [12]. 2

TABLE I LIST OF COMMONLY USED ACRONYMS IN THIS PAPER

Acronym Explanation DASH Dynamic Adaptive Streaming over HTTP ABR Adaptive BitRate QoE Quality of Experience DL Deep Learning RL Reinforcement Learning 5G 5th Generation mobile networks LTE Long-Term Evolution MIMO Multi-Input Multi-Output IoT Internet of Things HTTP HyperText Transfer Protocol DRL Deep Reinforcement Learning MAC Media Access Control MDP Markov Decision Process DQL Deep Q-Learning CNN Convolutional Neural Networks OFDMA Orthogonal Frequency-Division Multiple Access OFDM Orthogonal Frequency-Division Multiplexing HAS HTTP Adaptive Streaming PoPs Point-of-Presence CDN Content Delivery Network MPTCP Multi-Path TCP EC Edge Computing SILP Stochastic Integer Linear Program MEC Mobile Edge Computing MNOs Mobile Network Operators KPIs Key Performance Indicators DNN Deep Neural Network SDN Software Deﬁned Networks DRNN Deep Recurrent Neural Network

However, most of the studies show QoE is still While, there exist previous surveys, in the area unsatisfactory [13]. of Internet video and optimizing applications for New applications of video over mobile client wireless networks in our opinion there are none are getting popular [14]. Exponential growth of which focuses on mobile video streaming algo- IoT based networks will increase these inno- rithms. Previous surveys like Seufert et al. [19] vative scenarios, with the applications like on- and Bentaleb et al. [20] discuss different ABR line object detection [15, 16] and energy efficient algorithms in general and related influence fac- scheduling [17]. Many new applications apply tors. In another survey by Juluri et al. [21], they machine learning algorithms like deep learning discussed tools and measurement methodologies [18] on video streams on resource constraint mo- for predicting QoE of online video streaming bile devices. These applications introduce new services. Similarly in [22], authors provide a sur- challenges and opportunities for Internet video vey of QoE models for ABR applications. Kua ecosystem. et al. [23] focuses on rate adaptation methods for Internet video in general, provides a com- B. Prior Survey Articles prehensive review of video traffic measurement methods and a set of characterization studies Having established the importance of ABR al- for well-known commercial streaming providers gorithms optimizing QoE for wireless and mobile like Netflix, YouTube, and Akamai. The survey clients in particular, in this paper, we are review- in [24] discusses the growing popularity of deep ing existing models and algorithms in this area. 3 learning (DL) based techniques to solve different upcoming new standards and their adapta- wireless network problems. They discuss the tions will create challenges for existing ABR applications of DL methods for different layers algorithms. We discuss suggestions for future of the network, but do not include Internet design of ABR algorithms. video and its challenges in particular. Seufert • We review many open-source implementations et al. in [25] focused on video quality metrics of different ABR algorithms. And we present and measurement approaches that are related their differences and comparisons. to HTTP based adaptive streaming. Similarly, A list of acronyms used throughout the article Barakabitze et al. [26] focused on techniques of is presented in Table I. The rest of this paper maintaining QoE in emerging types of networks is organized as follows. Section II presents the based on SDNs and NFVs. They do discuss overview of the Internet video delivery ecosys- QoE for multimedia application in LTE and tem. It also introduces basics of machine learn- 5G networks but more with the context and ing techniques used in the papers discussed in opportunities related to SDN/NFV. this survey. Section III surveys the bitrate adap- Bentaleb et. al. in their survey [27] describes tation algorithms for wireless networks. This sec- a many recent paper related to ABR in detail. tion is divided in different subsection according Their main focus is a scheme classification based to the type of algorithms. Section IV presents on the unique features of the adaptation logic of different open challenges in the area of ABR ABR algorithms. for wireless. In the section V discusses different Our survey is unique from others in three open-source implementations of ABR algorithms key aspects: (1) It is focused on clients con- and also different dataset available for experi- nected to the internet using wireless technologies ments. Finally, Section VI provides concluding like WiFi or cellular network. In all previous remarks. surveys none focused on different schemes and their challenges of designing ABR specifically for wireless networks. (2) We provide an in- II. Wireless ABR Video Streaming: An dept survey of schemes using machine learning in Overview general and RL in particular to optimize QoE of A. Why video on wireless is different? video for wireless nodes. (3) We provide many Internet video systems are designed to cope open challenges in designing ABR for future with the inherent variability in network con- wireless networks. ditions. Media players at the client implement ABR algorithms [9, 28, 29]. There are a vari- C. Contributions of This Survey Article ety of protocols like MPEG-DASH [30], Ap- In this article, a comprehensive survey on the ple HLS [31], Microsoft Smooth Streaming [32], proposed ABR algorithms for wireless networks Adobe HDS [33] etc. that adopt HTTP based is presented. The contributions of this review adaptive video streaming. These protocols are paper are summarized as follows. Towards this called Dynamic Adaptive Streaming over HTTP end we present in this paper a review of the (DASH). In these schemes, server splits each proposed ABR algorithms for wireless networks. video into multiple segments with uniform play- • We present and classify the existing works back time (typically 1 to 10 seconds). After- related to ABR for wireless networks. In this wards, the server encodes these segments into paper, we provide an overview of the current multiple copies with different discrete encod- state of the art in the field of Internet video ing bitrate levels having different sizes. Before in wireless networks. a DASH video session starts, a client obtains • We identify some important directions of fu- the available bitrate map from the server. To ture research. We present some area where download each segment, the client needs to send 4 an HTTP request to the server, and specify the contrast to MPC, it does not employ throughput bitrate level it prefers for that segment. prediction in making decisions. It tries to avoid Most of the content publishers in today’s re-buffering by maintaining a minimum buffer Internet serve their videos from some popu- threshold. This threshold can be used to make lar content delivery networks (CDNs). These this algorithm conservative or optimistic about CDNs have point-of-presence (PoPs) in many the future bitrates. different geographical and network domains. By Recently, RL and other machine learning tech- using their PoPs and their peers, CDNs reduce niques are used to design ABR algorithms [37– the cost of serving videos and join times, each 39]. Using RL Pensieve [5] was able to outper- video is delivered over an ISP network. Most form the state-of-the-art. Oboe [6] presented an of these Internet service providers (ISPs) have ABR algorithm which performs an automatic two parts, the core network and the radio-access tuning of configuration parameter values for network (e.g., cellular network) as shown in each network state independently. This allows Figure 1. User devices are connected with the Oboe to give better QoE than Pensive. ISP via wireless technology like WiFi or LTE. However, these solutions render unsatisfactory B. Why Reinforcement Learning is popular for performance in WiFi or LTE networks. ABR design? ABR algorithms work by (a) chopping the There are many schemes based on learning- video into chunks, each of which is available at based approach to solve problems in networks in a range of bit rates; then (b) choosing which bit general [40]. Some of them are focused towards rate to fetch a chunk at based on conditions applying RL. In the past, it has been noted that such as the amount of video the client has RL is very suitable to be applied to many com- buffered and the recent throughput of the net- puter network problems. RL is quite a natural work. These ABR algorithms are implemented way to model an optimization control problem in video clients. Hence, on a mobile client they [41]. must be energy efficient in addition to all other There are two main entities in a RL problem properties like computational efficiency and op- Figure 2, Agent and Environment. Agent ob- timize QoE for the user. serves the state si of the environment at each One of the first ABR algorithms was de- interval and then choose an action ai. Agent signed in model predictive control (MPC) [34]. It receives a reward ri for his action which can be predicts throughput of future chunk downloads positive or negative. The goal of an agent in RL using the historic data of recently downloaded is to maximize the cumulative reward defined as chunks. The predicted value of throughput is follows: used to select the bitrate for the future chunks such that optimizes the QoE function. MPC has " ∞ # X i a look-ahead window of 5 future chunks. There Vπ(s) = E γ ri|s0 = s is an aggressive version of this algorithm called i=0 FastMPC which directly uses the throughput Where π is the policy function πi(si, ai). It estimate obtained using a harmonic mean pre- gives the probability distribution of the current dictor. state and action. While the γ is the discount On the other hand there are algorithms like factor for the future reward. Hence, an RL agent Buffer Occupancy based Lyapunov Algorithm learns optimal policy to maximize its rewards. (BOLA) [35] which uses buffer occupancy to To learn the optimal policy most of the RL selects bitrate. BOLA solves an optimization applications use Q-learning. problem to select optimal bitrate. BOLA is a In Q-Learning, each pair of state and action buffer-based algorithm used in Dash.js [36]. In (s, a) is mapped to a value under a policy π. This 5

Fig. 1. Mobile video player architecture value is the expected total reward of taking an action a in a state s.

" ∞ # π X i Q (s, a) = E γ R(st, π(st))|s0 = s, a0 = a i=0

The goal of Q-learning algorithm is to find the policy to maximize this function. In DRL an agent is learning this optimal policy using a deep neural network (DNN). So, in DRL approximate Fig. 2. Overview of Deep Reinforcement Learning value functions called deep Q learning function is used. This function is learned by gradient method used in deep learning. Here the agent n i interacts with the environment like in RL and X X QoE = q(Ri) − µ Ti uses its reward as the training input for the deep i=1 i=1 neural network. The goal during training of this n−1 X DNN is to optimize its parameters. Hence, it − |q(Ri+1) − q(Ri)| selects actions that can result in the best future i=1 return. Where the function q is an increasing function Pensive [5] was the first paper to use DRL in of bitrate selected Ri for the interval i so, designing ABR. Pensive model it using DRL so higher the bitrate higher the reward. The second it can be independent of the assumptions taken term depends on the time taken to buffer that by the designer of ABR schemes. In Pensive segment Ti. This will penalize the reward for algorithm, they defined the QoE as the reward any re-buffering required before playing the next of the ABR algorithm working as an agent in segment. Last segment of the reward function is the DRL. They modeled their generic QoE based penalizing for lack of smoothness. If the bitrate reward function as follows. of the video change from the previous segment, 6 then user will observe some lack in smoothness. Pensive [5] is used in many similar research for different variants of the problem. There are two main types of the RL based on the training. First is model based RL and the other is model free RL. 1) Model-free Reinforcement Learning: Model-free RL learns directly from the experiences while in training. The states and transition probabilities of the underling Markov decision process (MDP) are unknown. Fig. 3. Model-free and Model-based Reinforcement In ABR, we have no prior model of QoE Learning dependence on the different state variables. Model-free RL learns in more interaction with the environment as compared to the model- Mobile Network Operators (MNOs) offer dif- based RL Figure 3. It is free from the biases of ferent packages to increase their number of the supervised training data. users. In some packages, they offer not to count 2) Model-based Reinforcement Learning: In certain services like Facebook, WhatsApp and this type of RL we have a prior model of Netflix toward monthly data quota. But they the system MDP. This will reduce the time of limit the rate of the users toward those ser- learning and cost in-terms of interactions with vices. Traditional ABR based services does not the environment, on the other hand, it require account for this rate limiting. Here traditional designer to provide or learn the model before throughput maximization based ABRs will not the start of the training. RL training phase will perform well. Zero-rated QoE [42] proposed a only optimize or refine this model. In this case, if novel approach which uses the collaboration of there are inaccuracies in the model, then this will the content provider and MNOs. They designed lead to degradation in quality of final output. an ABR which focuses on improving QoE in In model-based RL, policy is learned through these special scenarios. They implemented their supervised learning. Then planning over the approach in a simulated environment and per- learned model is done in second phase. Conse- formed evaluation with the baseline. quently, model-based algorithm uses a reduced number of interactions with the real environ- A. Machine Learning based ABR schemes ment during the learning phase 3. So, learning Comyco [43] is another study using the can be much faster because there is no need to Learning-based ABR. They discuss a few weak- get the feedback from the environment. On the nesses of previous RL-based ABR algorithms. downside, however, if the model is inaccurate, Their measurement study shows that the quality we risk learning something completely different of video presentations is not always maximized from the reality. by QoE metrics based on only video bitrates, re- buffering times and video smoothness. They pro- III. Different types of ABR algorithms posed Imitation Learning instead of supervised There are different types of research in this learning can address these weaknesses. area. Some have designed ABR algorithms for Reinforcement learning is also applied to make mobile nodes to incorporate the movement of video streaming appear more smooth to the user. the nodes, while others focused on the resource constraints like spectrum and energy of the video client. 7

TABLE II A SUMMARY OF RELATED PAPERS AND MAIN IDEA.

Paper Main Idea Is ABR? For mobile? FESTIVE [34] Stateful ABR with randomized chunk scheduling to avoid synchronization biases X Pensive [5] DRL is used to optimize the QoE X Oboe [6] Combine offline and online tunning of the parameters X pstream [12] Improves the QoE by taking advantage of the PHY information of LTE networks X X MP-DASH [44] Use MPTCP to schedule X X Wi-Fi Goes to Town [45] Improves the QoE during high speed handovers X HotDASH [46] Use DRL to detect user specific important part of video to improve their quality X Zero-rated QoE [42] Collaboration of MNOs and content providers to improve QoE for rate limited users X X QARC [47] Imrove the perceptual quality of the video instead of traditional QoE metrics X Bursttracker [13] Find the bottleneck in the video streaming over the LTE network X Qflow [48] Used both Model based and Model free DRL X X NAS [49] A deep neural network based ABR X X IncorpPred [50] Incorporate cellular throughput prediction to improve ABR X X Comyco [43] Proposed imitation based Learning instead of supervised learning X Jigsaw [51] 4K video streaming X TransPi [52] Introduced hardware-assisted video transcoding for Wireless X X CASTLE [53] Client schedular to minimizes Load and Energy at the same time X X ACAA [54] Incorporate user’s subjective viewing information to improve ABR X LinkForecast [55] Bandwidth prediction for LTE network X QUAD [56] Reduce the bandwidth usage while maintaining high QoE X X 8

In [57], the authors design an optimization networking, like in network security and user problem for ABR to exploit power control over localization. multiple sub channels at the transmitter in such In QARC [47] they have designed a rate con- a way that video quality remains smooth.It pe- trol algorithm that is focused on the perpetual nalizes both for buffer underflow and overflow. quality of the video. The perpetual quality, of Then, they mapped this constraint optimization the video is defined as how many objects are in problem into a MDP. The MDP is solved using the image and how bright or dark it is. For low reinforcement learning techniques. perceptual quality parts of the video we can save It is challenging to implement heavyweight the bandwidth and delay by requesting video ARB techniques in resource constraint mobile at the low quality. While for high perceptual devices. PiTree [58] introduced the idea of us- quality, parts should be downloaded at a higher ing lightweight decision trees to simplify the bitrate. This can be achieved by lower sending complex and heavyweight neural network based rate and latency. QARC also use DRL to train techniques. PiTree give a highly scalable frame- the neural network to predict the future video work to convert complex ABRs into decision frames based on the perceptual quality of the trees. They also provide some theoretical upper previous frames. It employs two-fold training of bound on the optimization loss during the con- DRL one for prediction of perceptual quality and version. the second one using A3C based asynchronous Challenges of Video streams with high quality training technique to train the actual RL algo- are increased in the case of remote drone pilot- rithm. They did trace driven analysis of their ing. The study in [59] discusses these challenges. techniques and compare it with Google Hangout It suggests decreasing the coupling of different and compound TCP. functional blocks. They proposed to use edge- computing elements in addition to adapting for B. Egde computing based ABR systems network conditions. Increasing demands of lower network delay QFlow [60] paper used reinforcement learning and higher data transmission rate are getting to perform one-way adaptive flow prioritization difficult meet from traditional ABR systems. at the edge network. QFlow argued current Recently, edge computing (EC) Figure 4 based link are application agnostic in their schedul- optimizations to ABR systems have been pro- ing. By making these links intelligently adapt posed to meet these challenges. In the paper [61], for different type of traffic leads to a better authors present some of the challenges and lim- QoE of the video streaming. QFlow borrowed itations of the current ABR applications. They concepts of network level priority queues from proposed an edge computing based solution to software defined networks (SDNs) and apply address these challenges and limitations. QFlow it to PHY/MAC layer using Software-Defined [60] is an Edge computing based ABR system Radios. using ML based model to optimize QoE. QFlow uses RL to optimize the QoE for video ShareAR [62] is a multi-user augmented re- by adapting configurations. QFlow uses both ality (AR) system which uses edge nodes to model-free and model-based RL approaches. optimize QoE for the user. The main challenge According to their evaluation, RL based ap- in multi-user AR is the communication between proach not only improves the QoE but also AR platforms. There are no prior work involving results in better buffer state and lower stall dura- data transmission in between AR devices and tion. The survey [40] provides a comprehensive their impact of the QoE. In multi-user AR, de- survey of deep learning-based techniques used vices can have different fields-of-view. They need in different wireless networking scenarios. It also to render their respective FoVs. In their system, highlights some potential applications of DL to they overcome these challenges and implemented 9

Fig. 4. Introduction of MEC to improve the ABR based video streaming a prototype of the system using two Android One way to decrease the latency of the system devices and an edge server. is to use MEC servers for video caching. Tran In FlexStream [63] they leveraged the SDN et al. [65] investigates a novel caching scheme functionality to get the benefits of centralized using multi-server MEC systems. Their systems management of distributed components. Here use two timescales. They formulated stochastic they use wireless edge device like AP as SDN integer linear program (SILP) to integrate these controller. They have implemented their system two timescales of long-term caching and short- as a light weight controller. In there evaluation, term video retrieval mode. By using simulations they showed FlexStream can achieve appropriate they showed the effectiveness of their system by bandwidth distribution. reducing access delay and increasing cache hit Blockchain technology is used by decentral- ratio. ized peer-to-peer video streaming systems to PrivacyGuard [66] is a system designed and monetize using smart contracts. In these new developed to obfuscate the activities of sensitive video streaming systems, content creators, con- IoT and mobile applications from attacks over sumers and advertisers can communicate with WiFi network. They have implemented a pro- each other without the help of a trusted third totype this systems on Android mobile devices party. There some challenges in designing these that to apply application level traffic shaping systems like processing and publishing of the and IP-sec tunneling schemes. video content in these systems. In [64], they propose using edge computing servers to of- C. ABR with different optimization goals fload these computationally intensive tasks. In Wi-Fi Goes to Town [45], implement a They proposed to employ edge servers through WiFi based hotspot network using picocell size distributed block-chain based incentive mecha- access point networks along the road to sup- nisms. port vehicular communication over high speed. Mobile Edge Computing (MEC) is getting They implemented optimized version of IEEE popular to provide low-latency ABR service. 802.11k and 802.11r standards. Although their 10 main focus is not video streaming but most of ferent approach to optimize video quality over their evaluation is done over video. Their scheme mobile devices. Their focus is on leveraging the provides more reliable video stream for high availability of multi-path in many common mo- speed mobile client. Also it improves the QoE bile devices like cell phones. They have WiFi metrics for the video like rebuffed ratio. card and LTE modem at the same time. So, in Sengupta et al. in HotDASH [46] focused on many cases it is possible to use LTE opportunis- improving the video quality for the specific user tically. They used Multi-Path TCP (MPTCP) to requirements. In most of the video streaming implement their approach. It prefers WiFi over situations there is some content of the video LTE when at home. The evaluations were done which is more important for the user. They in both controlled setting and in the wild. Trace use DRL to detect that part of the video and of throughput and RTT of the WiFi networks at then the requirement, therefore, is for a video 33 locations in the US for the evaluation. MP- streaming strategy to into account the content DASH is impelled using GPAC. FESTIVE and preferences of the users. So, ABR will be aware BBA is implemented over the multi-path sched- of the high-priority temporal content. ABR try uler. In [68] they implemented traffic offloading to pre-fetch those high priority parts of the video build on the MP-DASH approach for general at much higher bitrate. HotDASH maximizes application. the content preferences of users, in addition to ACAA [54] is a scheme focused towards se- optimal use of bandwidth. They implemented mantic information of video content. Recently their scheme in dash.js and compared it with the ABR researchers are designing with user’s sub- six baseline algorithms like FESTIVE, FastMPC jective viewing information to improve the QoE and PENSIVE. of the video specific to the user requirement. Most of the ABR algorithms are not designed ACAA use the research on video affective con- with the consideration of data consumption. But tent analysis. It incorporated individual user most cellular customers have limited data in preferences into the bit-rate adaptation decisions their monthly data plan. According to [67] aver- to improve the QoE. Identify the user relevant age U.S. cellular customer has only 2.5 GB per parts of the video and then assign bit-rate bud- month data plan, while one hour high definition get according to it. They compared their scheme (HD) video on mobile require 3 GB data. QUAD with BBA and buffer-based adaptation (BBA), [56] focuses on reduce the bandwidth usage while and model predictive control (MPC). ACAA maintaining high QoE for the user. Their scheme implemented their scheme with the DASH client is also energy efficient because it requires to in accordance with [69] to perform trace-driven download less amount of data. evaluation platform with python 2.7. QUAD introduced a novel Chunk Based Fil- tering (CBF) approach which leverages two fun- D. Measurement of different ABR schemes damental tradeoffs of video quality and bitrates. Many papers study performance of different First, higher bitrate leads to diminishing re- ABR algorithms and make a comparative study. turn in terms of video quality. Second, dif- Some of them performed active measurement ferent chunks have different impact on video [70] [71] while other perform passive measure- quality. Their scheme selects chucks to maxi- ments [72]. There are others like [73] and Puffer mize the QoE while keeping the data consump- [74] made a database of different ABRs. Duane tion minimal. QUAD implemented its scheme et al. developed the Waterloo SQoE-III database in both dash.js and ExoPlayer and perform [73]. This database provides a subjective evalu- evaluations. They compared their approach with ation of different QoE models and ABR algo- RobustMPC and PANDA. rithms. SQoE-III evaluated Rate-based, AIMD, At the same time MP-DASH [44] take a dif- Dynamic Adaptive Streaming algorithm, etc in 11 their paper. According to their evaluation 5 supporting these data rates. These devices work out of 6 models are quite close in terms of in 60GHz spectrum. In this range, transmis- performance. In addition to the experimental sion is highly sensitive to mobility. There can evaluation of other ABR techniques, Puffer [74] be drastic change in the throughput for minor developed a live TV streaming website. This movement. In the case of blocking, the line of prototype website has attracted over 100,000 sight throughput might be affected and reaches users across the Internet. This system works as a to zero. randomized experiment; one set of ABR schemes In the presence of these large throughput is randomly assigned to each session. variations, traditional video codecs like H.264 Haung et al. [71] is the first study performed and HEVC become infeasible. To overcome these in this area. In their study, they perform a limitations, layered video codecs are used by measurement study of three popular video ser- Jigsaw [51]. It uses scalable video coding (SVC) vices Hulu, Netflix, and Vudu. [75] studied and which is an extension of the H.264 standard. discussed the impact of QUIC on QoE of the The study in [51] use fast encoding schemes and popular ABR. It also discussed how can existing implement it using new layered video coding ABRs leverage the potential benefits of QUIC. methods. The study in [76] is a general measurement Panoramic video is another emerging appli- study of application performance in the rapid cation of video streaming, it is known as 360◦ deployments of LTE networks. Data traces has video. Platforms like Facebook and YouTube been used from different major LTE providers. also support them. Flare [78], presented a practi- VideoNOC [70] is an passive measurement study cal prototype of a 360◦ video streaming solution of Video QoE for Mobile Network Operators using commodity devices. This study predicts (MNOs). VideoNOC presented an approach to future behavior of the user to fetch only the assess the QoE for different MNOs using ob- relevant portions of the video to cover the view jective metrics for video quality. To get an ob- of the user, which enables Flare to reduce the jective estimate of QoE metric they collected bandwidth usage of the system significantly. HTTP/S traffic in the core of the LTE network. This viewport-adaptive 360◦ streaming is an VideoNOC performed many efficient and scal- establish technique. Flare is the first complete able cross-layer analytics over these logs. working implementation on commodity mobile Recently, a third-party based system to is de- phone. It uses a online machine learning (ML) signed to evaluate and understand the behavior algorithm to predict head movement of the user of different closed source ABR based streaming which changes the users’ future viewpoint. services [77]. Channel State Information (CSI) Another 360◦ video streaming system is pre- can also able to understand the behavior of sented in Rubiks [79]. Rubiks discusses differ- ABRs in the presence of traffic encryption. ent challenges of implementing tile-based video streaming techniques used in different imple- E. 4K and 360-degree video streaming mentations to predict field of view (FoV) of 4K videos are now getting increasingly com- the user. In resource constraints of commodity mon. New applications like virtual reality (VR) smartphones, it is not possible to meet with the and augmented reality (AR) will make 4K ex- requirements these tile-based systems. tremely important in the coming future. These Rubiks uses HEVC to implement tile-based applications do not only require high resolu- streaming instead of H.264 [80] used by previous tion but also very low latency. In there raw system. HEVC [81] has a built-in tiling scheme form 4K video stream requires more than 2Gpbs to encode video data. Their system can stream physical data rate. Currently, IEEE 802.11ad different parts of the video at different bitrates. based WiGig card are commodity wireless cards This allows them to download tiles in different 12 quality according to their probability of viewing. F. Video stream in vehicular networks Managing the amount of data downloaded at the Video streaming in vehicular networks is even client it can control the decoding time. Decoding more challenging [85]. There are different types time increases substantially for 8K videos on of communications in V2X networks. In recent mobile device. years, many papers try to address these chal- In the paper [82], DRL is used to implement lenges [86, 87]. Some of them use Edge network- a panoramic video streaming system. Their sys- based caching techniques and other explored tem used DRL to optimize QoE using a broad learning-based approaches to optimize video set of features. Here their focus is on two main streaming. challenges of 360-degree video. First, there is Recent papers explore the use of different a large number of time-variant features which machine learning-based approaches to optimize needed to be adapted to achieve a reasonable video streaming in wireless networks [88–91]. quality. Second, QoE metrics are also different Some of them focus on the presence of IoT for different scenarios. Zhang et al. uses DRL devices on the same spectrum, others optimize to find an optimization model. This model finds for energy-efficiency. In [92], they perform an ex- the best rate allocation scheme for different perimental analysis of 10 widely deployed ABRs. scenarios. Their measurement shows none of the deployed ABRs focus on available bandwidth and some Tang et al. in [83] presented a promising ap- leave a large fraction of available network ca- proach to improve QoE for the user in a 360- pacity unused. degree video streaming system. Their focus is on a streaming a newly generated 360-degree video. In this case, there is no historical view- G. Optimizing video during handovers ing information available which can be used to Wi-Fi Goes to Town [45] was one of predict user viewing behavior. In these scenarios, the first research approaches to implement a there are additional challenges of learning FoV performance-tuned version of the IEEE 802.11r patterns online and also the lengths of these and 802.11k fast handover protocol. It try to FoV segments are also unknown in advance. The use Picocells to increase the capacity of the authors present OBS360 algorithm which is an network, which results in higher spectral effi- online bitrate selection algorithm to optimize ciency and throughput. In [45] focus is not on the user’s QoE in 360-degree video. OBS360 video delivery. Focus of [45] is on maximizing algorithm is able to learn user’s FoV preference throughput that can often lead to lower QoE and also the time-varying downloading capacity for video clients. Wang et al. [93] propose a of the user. real-time handover protocol called mmHandover for a 5G network working in mmWave. In the Perfecto et al. [84] they discussed Immer- past, there have been many efforts to design sive virtual reality (VR) applications. These mechanisms to predict handovers [94–96]. These applications require achieving motion-to-photon schemes try to predict handovers to improve the (MTP) delays, which are defined as end-to-end QoS of different traffic on mobile devices. latency of 15-20 milliseconds. Providing 360◦ video with these delay guarantees is quite a challenge. They applied a deep recurrent neural H. Understand the network bottleneck to im- network (DRNN) to predict the upcoming tiled prove ABR FoV. In addition to that, they exploit millimeter In their paper BurstTracker [13] focused on wave (mmWave) multicast transmission at the LTE networks. BurstTracker identify issues af- physical layer to improve the efficiency of the fecting the QoE of the video streaming perfor- system. mance. BurstTracker is able to identify a sur- 13

boxes for TCP connections, these middle boxes introduce serious performance bottleneck. PiStream [12] has determined total download resources allocated to the user on a LTE base station. BurstTracker is able to estimate it at the user and then use it to improve the estimation of bitrate. Pistream assumes in case of the bottleneck it is at the base station.

I. ABR algorithms with cross-layer optimizations In the ground breaking paper [12] PiStream Fig. 5. Resource Block and Resource Element in LTE the first presented a challenge faced by DASH networks players in LTE. LTE bandwidth is very high, around 10x than its predecessor 3G. Despite this high bandwidth video clients does not perform prisingly different bottleneck. BurstTracker un- well. PiStream motivated this problem with a derstand the scheduling pattern of the LTE base measurement study which shows how a DASH station. Their focus is to identify the occupancy client behaves in the LTE network. DASH uses of each user’s download queue. If this queue is a throughput estimator to predict the data rate. not empty for one scheduling cycle then access But in the LTE network this estimate is mostly link the bottleneck link. It means that data was an underestimate. This leads DASH selecting a in the queue of LTE base station and was not lower quality for the future. PiStream observe being delivered in the next cycle. If there are that in the LTE networks all the bandwidth so many cycles like that, they will decrease the information for the access link is known to the QoE for the video. LTE network. The approach takes advantage One of the main challenges, is that user queue of the Physical layer information to get an information is not available at the client. So, accurate estimate of the bandwidth. PiStream this approach designed a method to estimate implemented their scheme using SDRs at the this information. The approach observes that Physical layer and using GPAC player as the if a user is selected for transmission and its open source client. In their evaluations, they queue is full, then the LTE base station sched- compared their scheme with FESTIVE, BBA, uler allocates the complete millisecond duration and PANDA. resource block (RB). As shown in the Figure Recently, Raca et al. [97] designed a approach 5 a RB is the smallest unit of resources that to address the challenges to ABR video in can be allocated to a user. It consists of 180 the challenging environment of cellular network. kHz in frequency while 6-7 OFDM symbols in They observed in cellular network radio channel, time. In frequency it is further divided into 12 conditions and load on the cell are continu- sub-carriers of 15 kHz each. Using these insights ously changing. In addition to that, there is gap BurstTracker is able to find out most of times in the time scale among different components user queue becomes empty before the complete of the system. Transport layer protocols react allocation of the user queue. This suggests that at the granularity of hundreds of milliseconds, bottleneck is not at the base station radio link. while radio channel changes at the fraction of According to BurstTracker, most of the large millisecond. One the other side, base station LTE network providers use split-TCP middle can allocate resource in a bursty manner. ML boxes. Due to TCP slow start used by middle based approach is used to predict throughput 14 of the mobile devices in a LTE network. There tions. Using this measurement study as motiva- ML model is learned on cellular trace data. tion which shows benefits of sharing application Their approach shows the importance of radio layer throughput to the lower-layer. LinkFore- level metrics in the video streaming applications cast explore the idea of sharing lower-layer link [98]. Their technique is implemented in Android information to the application. video player (ExoPlayer) and performed evalua- LinkForecast designs a ML based framework tion on a real testbed. to predict link bandwidth in real time. This In [99], a dataset for 5G measurements is pre- framework combine both upper and lower layers sented. These measurements are performed on a information for future prediction. According to a major Irish mobile operator. In this dataset their evaluation this technique is not only more all the key performance indicators (KPIs) for precise but also lightweight and insensitive to client-side cellular metrics and throughput are the training data. collected. Dataset is collected with two differ- In [97] authors investigate further on the ob- ent mobility patterns of the user driving and servation of high variability of network condi- static. Also, stream content is generated from tions in cellular radio access networks. Publicly Amazon Prime and Netflix streaming device. available data sets [103] are used to learn a In this dataset, GPS based location informa- machine learning model. The data set is very tion is also present. This data is generated rich in terms of parameters and different mobil- from Android network monitoring application, ity patterns like static, walking, car and train G-NetTrack Pro. This application can run over etc. Random forest based technique is selected a non-rooted Android phone. as their ML model. They used random forest In addition to the real dataset, a second syn- (RF) as model for prediction. By increasing the thetic dataset is also presented in [99] . This data number of trees and using mean of all trees in set is generated from ns-3 [100] using a large- the RF as the predicted value avoid over-fitting. scale multi-cell 5G/mmwave framework. Parameters used in [97] model are available One of the first cross-layer ABR algorithm through Android Debug Bridge (ADB) APIs. for wireless network was CrystalBall algorithm Implementation is done on ExoPlayer and per- [101]. It is a two step algorithm to predict avail- formed evaluation on a real testbed. In there able bandwidth. Main ABR algorithm is based evaluation, they show the effectiveness of their on it. In this study, the effects of the prediction system by improving all QoE metrics. quality on the accuracy of the ABR. CrystalBall Chen et al. [107] authors proposed ABR can shows with error mitigation techniques some save energy during video stream if it consider level of prediction error can be tolerated. the context of streaming. It means if user is Another similar technique is CQIC [102] to watching a video in a room may have completely predict TCP throughput using Radio level in- different QoE requirements as compared to a formation in smart phones. user on a moving vehicle. Using traces they have Yue et al. [55] they showed even a trace of modeled the impact of vibration level in addition 500 data points can be used to develop an to video bitrate on the QoE and signal strength accurate model of LTE link level predictions on power consumption. They designed an opti- using a ML model. LinkForecast presented an mal algorithm using an optimization problem to extensive measurement study to understand the minimize energy consumption. bandwidth allocation algorithm of current cel- Raca et al. in [106] presents the effects of lular networks. Most of them allocate a fair the highly dynamic wireless communication on proportion of the available bandwidth. Available different application. Here they provide evidence bandwidth is calculated using the observations of PHY layer metrics are used in assigning re- of the recent past throughput and link condi- sources to the users in the cellular network. ABR 15

Based on DL [10, 11, 18, 40, 47] ABR based on ML [58, 59] Based on RL [104]

Egde computing based ABR systems Using SDN [60, 62, 65] [63, 66]

Improve QoE for vehicles [45, 86, 87, 93]

Optimize for user data-plan [67]

ABR with diﬀerent optimization goals Chunk Based Filtering Adaptive Bitrate Streaming (ABR) [56]

Optimizing video during handovers [45, 93, 96]

Video stream in vehicular networks [85–87, 105]

4K video streaming [51] 4K and 360◦ video streaming 360◦ video streaming [78, 79, 82–84]

Cross-layer optimizations for Video streaming [55, 97, 106, 107]

Fig. 6. Major categories of ABR for wireless networks in the literature is used as an example application to explain the than 98% recently [108]. But most of these de- advantages of using AI based techniques to learn vices are low end devices with slow network like accurate throughput prediction using PHY layer 2G [109]. But video is the most dominant type level metrics in cellular networks. of traffic in these parts of the world. This creates We classify the different ABR for wireless an even higher level challenge for Internet video schemes into five main categories see Figure 6. providers [110]. These categories are based on techniques used to design ABR or different types of objectives B. Providing effective ABR for developing world used in the design of the algorithms. Similarly New services like Web Light and Facebook’s the reviewed major approaches are summarized Free Basics service are introduced to improve along with the references in Table II. the Internet quality and availability. Now, Free basics service is expanded to over 60 countries IV. Open challenges and opportunities [111] across select cellular service providers. But A. Improving video for developing world clients these services do not handle video elegantly. Mobile phone adoption is even more explosive Both of these services replacing videos with an in developing countries. It has reached more image [112]. 16

In future, demand for better quality Internet In their measurements Nexus 5 phone with video will increase more for these low resource 2GB memory led to frequent video player developing world clients. It is a challenge to crashes when it plays a 1080p video. To achieve provide even bare minimum service to these high QoE under low-memory scenarios they pro- clients [108]. But one can use techniques like posed an new scheme DAVS which adapt the Oboe in [6]. There are some proposed schemes playback buffer size based on conditions based which incorporate device level characteristics to on device memory pressure. improve the selection of bitrate [113]. In the developing world, vast amount of data access and high speed both are a luxury. Most of C. Optimize the spectral efficiency for video traf- the communication is through text-based media fic like posters and flyers. Most of the users in the developing regions are illiterate and resource- The consistent exponential growth of video constraint in terms of poor connectivity and traffic will increase in the future with the advent little exposure to the technology. Access to com- of 5G based IoT devices. WiFi alliance has puter and laptops is also very limited. Most recently approved WiFi-6 [118] (802.11ax) with of the Internet access is through low-end cell- the focus of high density WLANs. These new phone with a low-end camera. There has been changes will lead to even more congestion in the many novel applications designed to solve dif- available spectrum specially in the unlicensed ferent developing world specific problems. Many domain. There have been many recent studies of these applications depends on video based to understand and optimize wireless spectrum solutions [114, 115]. Video streaming patterns of sharing between different technologies like LTE community health workers in Africa is studied or 5G based cellular networks and WiFi in unli- in [116].They demonstrated the effectiveness of censed bands [9, 28, 29, 119]. Some of them used health videos and also presented lessons for a machine learning-based approach to optimize projects seeking to use multimedia content in spectrum sharing [10]. But most of these papers rural setting. are optimizing at the network level. This leads AudioCanvas [117] is an application created to many lost opportunities specifically related to for rural developing regions. It can be used by video streaming [88]. telephone as an audio information system. This system enable rural users to interact directly with their pictures and receive narration or de- D. Improving the QoE in the presence of network scription. handovers In a recent paper [113] authors have studied effects of memory pressure on video streaming Some recent measurement studies [105] shows applications. Their experiments suggest QoE of that current policies of cellular carriers are not video streaming is significantly affected by the optimized, especially during handovers. These selecting higher nitrates in a low memory cell- policies do not consider cell load information phone. On the other hand [109] presents a com- during handover resulting in degradation of ap- prehensive measurement study of cell phones plication performance. In a 5G small cell, han- used by users in developing world. Dataset used dovers will be more frequent. Figure 7 shows a in [109] has less than 1% of the cellphone users in typical scenario of small cell based network in the developing world have more than 2GB mem- 5G vehicles in these small cells will experience ory in there devices. This creates a challenge frequent hand-offs. It will be critical for video of designing specialized ABRs for developing streaming applications to perform well during world. these handovers. 17

Fig. 7. Presence of Small Cells and frequent handovers in 5G

V. Open-source implementations of TABLE III ABR algorithms and datasets Summary of open-source ABR implementations There are many open-source video players Implementation Corresponding papers Pensive [5],Oboe available on the Internet. But dash.js and Ex- [6],HotDASH [46], oPlayer [120] are the most popular in the in- Dash player Bursttracker [13], dustry and research. ExoPlayer is developed QUAD [56] and NAS [49] by Google as the first Android-based mobile GPAC video player [44] and Pstream [12] DASH player. Many research paper have used IncorpPred [50] and ExoPlayer it as their reference to implement their ABR QUAD [56] QARC [47], Comyco algorithm. The other popular implementation Trace driven [43], and ACAA[54] is dash.js [121]. It is developed by DASH In- dustry Forum which is supported by most of the major players in Internet video industry like Akamai [122]. Previously GPAC [123] was also implementation used in different papers. It is very popular in research for prototyping. It is evident that most popular implementation in now called MP4Client. Both GPAC and DASH research is dash.js. Even in wireless network are implemented in JavaScript. In comparison based evaluations [13, 56] it is more commonly to all the open-source implementations, dash.js used due to its flexibility and acceptance. There encapsulates the standard and best practices. It are hundreds of different ABR algorithms im- is easy to customize and there is an Akamai plemented using these open-source players. One reference implementation also available online of the popular one is of Pensive and their data which makes it easy to test. There are libraries traces [124]. Recently NAS [49] implementation available to use this for trace-driven analysis. is also available open-source [125]. Video providers wishing to use DASH often There are two open-source data sets available use the reference client dash.js to build their [103, 126] for trace-based analysis and to train own video players. Table III shows open-source machine learning algorithms. They are available 18

in multiple encoding. These encoding rates are [4] V. S. Florin Dobrian, “Understanding the impact of comparative to the large CDN provider like video quality on user engagement.” in SIGCOMM, 2011., 2011. Hulu, YouTube and Netflix. [5] R. N. Hongzi Mao and M. Alizadeh, “Neural adap- The testbed used to collect measurements tive video streaming with pensieve,” in ACM SIG- in [126] is based on “MP4Client”, a multime- COMM 2017., 2017. [6] Z. Akhtar, Y. S. Nam, R. Govindan, S. Rao, dia player based on GPAC [123]. They utilised J. Chen, E. Katz-Bassett, B. Ribeiro, J. Zhan, very well known animated videos like Elephant and H. Zhang., “Oboe: auto-tuning video abr algo- Dreams, Big Buck Bunny etc. Their files are rithms to network conditions.” in ACM SIGCOMM 2018, 2018. obtained as 920x1080 YUV files. [7] V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lil- Similarly the second dataset [126] is a trace licrap, T. Harley, D. Silver, and K. Kavukcuoglu, of 4G dataset which is composed of key perfor- “Asynchronous methods for deep reinforcement learning,” in Proceedings of The 33rd International mance indicators (KPIs) from two major Irish Conference on Machine Learning, ser. Proceedings cellular providers. They are collected with dif- of Machine Learning Research, M. F. Balcan and ferent mobility patterns like static, car, bus and K. Q. Weinberger, Eds., vol. 48. New York, New York, USA: PMLR, 20–22 Jun 2016, pp. 1928– train. It has a very diverse range of throughput 1937. from 0 to 173 Mbit/sec. These traces are gener- [8] V. Mnih, D. S. Koray Kavukcuoglu, A. A. Rusu, ated from a well-known Android network moni- J. Veness, M. G. Bellemare, A. Graves, M. Ried- miller, A. K. Fidjeland, and G. Ostrovski, “Human- toring applications. There are few limitations of level control through deep reinforcement learning.” this dataset first all of its samples are of 1sec in Nature, 2015., 2016, pp. 1928–1937. duration. Also, there is no GPS information in [9] S. Mustafa, K. A. Alam, B. Khan, M. H. Ullah, and P. Touseef, “Fair coexistence of lte and wifi-802.11 this dataset. To supplement the limitations of in unlicensed spectrum, a systematic literature re- the real dataset there is repository of synthetic view,” in ACM ICFNDS 2019., 2019. dataset. [10] M. Alsenwi, I. Yaqoob, S. R. Pandey, Y. K. Tun, A. K. Bairagi, L. Kim, and C. S. Hong, “Towards coexistence of cellular and wifi networks in un- VI. Conclusion licensed spectrum: A neural networks based approach.” in IEEE Access 2019, 2019. In this paper, we surveyed different key tech- [11] M. A. et al., “Towards coexistence of cellular and niques in the area of Internet video for wireless wifi networks in unlicensed spectrum: A neural networks based approach,” in IEEE Access 2019, networks. It was observed that many approaches 2019. used cross layer communications on the clients [12] X. Xie, X. Zhang, S. Kumar, and L. E. Li, to improve the re-buffering, quality switching “Pistream: Physical layer informed adaptive video streaming over lte,” in ACM MobiCom 2015, 2015. and encoding related impairments of the mobile [13] A. Balasingam, M. Bansal, R. Misra, K. Nagaraj, video. It is important to note that with the R. Tandra, S. Katti, and A. Schulman, “Detecting upcoming deployments of WiFi-6 and 5G new if lte is the bottleneck with bursttracker,” in ACM Mobicom 2019, 2019. challenges will arise which require us to rethink [14] X. Ran, H. Chen, X. Zhu, Z. Liu, and Jiasi, “Chen. the implementation of ABR algorithms to ad- deepdecision: A mobile deep learning framework dress these challenges. for edge video analytics.” in In Proc. IEEE Interna- tional Conference on Computer Communications, 2018, 2018. References [15] Y. Yuan, X. Liang, X. Wang, D.-Y. Yeung, and A. Gupta., “Temporal dynamic graph lstm for [1] Cisco VNI: Global Mobile Data Traffic Forecast action-driven video object detection.” in In Proc. Update, 2016- 2021. IEEE International Conference on Computer Vi- [2] CSandvine: The global internet sion (ICCV) 2017, 2017. phenomena report. [Online]. Available: [16] K. C. Zongqing Lu, Noor Felemban and T. L. https://www.sandvine.com/press-releases/ Porta., “Demo abstract: On-demand information sandvine-releases-2019-global-internet-phenomena-report retrieval from videos using deep learning in wireless [3] Y. Huang, “Confused, timid, and unstable: Picking networks.” in In Proc. IEEE/ACM Second Inter- a video streaming rate is hard.” in ACM IMC, 2012, national Conference on Internet-of-Things Design 2012. and Implementation (IoTDI), 2019. 19

[17] Q. Zhang, M. Lin, L. T. Yang, Z. Chen, and P. Li., [Online]. Available: https://developer.apple.com/ “Energy-eﬃcient scheduling for real-time systems streaming based on deep q-learning model.” in IEEE Trans- [32] “Microsoft, “microsoft smooth streaming.” [On- actions on Sustainable Computing, 2017., 2017. line]. Available: http://www.iis.net/downloads/ [18] S. Rallapalli, H. Qiu, A. Bency, S. Karthikeyan, microsoft/smoothstreaming. R. Govindan, B. Manjunath, and R. Urgaonkar., [33] “Adobe, “http dynamic streaming,”.” [On- “Are very deep neural networks feasible on mobile line]. Available: http://www.adobe.com/products/ devices.” in IEEE Transactions on Circuits and hds-dynamicstreaming.html. Systems for Video Technology, 2016, 2012. [34] V. S. Xiaoqi Yin, Abhishek Jindal and B. Sinopoli., [19] S. E. M. Seufert, M. Slanina, T. Zinner, T. Hoßfeld, “A control theoretic approach for dynamic adap- and P. Tran-Gia, “A survey on quality of experi- tive video streaming over http.” in In Proceedings ence of http adaptive streaming,” in IEEE Com- of the ACM Conference on Special Interest Group mun. Surveys Tuts., 2015., 2015. on Data Communication, SIGCOMM, 2015., 2015. [20] A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, [35] R. U. Kevin Spiteri and R. K. Sitaraman., “Bola: and R. Zimmermann, “A survey on bitrate adap- Near-optimal bitrate adaptation for online videos.” tation schemes for streaming media over http,” in In Proceedings of the IEEE International Con- in IEEE Communications Surveys Tutorials 2019, ference on Computer Communications, INFO- 2019. COM, 2016, 2016. [21] V. T. P. Juluri and D. Medhi, “Measurement of [36] DASH Industry Forum. [Online]. Available: https: quality of experience of video-on-demand services: //github.com/Dash-Industry-Forum/dash.js A survey,” in IEEE Commun. Surveys Tuts., 2016., [37] L. T. Federico Chiariotti, Stefano D’Aronco and 2016. P. Frossard., “Online learning adaptation strategy [22] N. Barman and M. G. Martini, “Qoe modeling for for dash clients.” in In Proceedings of the Interna- http adaptive video streaming–a survey and open tional Conference on Multimedia Systems, MMSys challenges,” in IEEE Access, 2019, 2019. 2016., 2016. [23] J. Kua, G. Armitage, and P. Branch, “A survey of [38] M. Claeys, S. Latré, J. Famaey, T. Wu, W. V. Leek- rate adaptation techniques for dynamic adaptive wijck, and F. D. Turck., “Design and optimisation streaming over http,” in IEEE Communications of a (fa)q-learning-based http adaptive streaming Surveys Tutorials, 2017, vol. 19, no. 3, 2017, pp. client.” in Connection Science, 26(1):25–43, 2014., 1842–1866. 2014. [24] Q. Mao, F. Hu, and Q. Hao, “Deep learning for [39] J. C. Virginia Martín and N. García., “Design, intelligent wireless networks: A comprehensive sur- optimization and evaluation of a q-learning http vey,” in IEEE Communications Surveys Tutorials, adaptive streaming client.” in IEEE Transactions 2019. on Consumer Electronics, 62(4):380–388, 2016, 2016. [25] M. Seufert, “A survey on quality of experience of [40] C. Zhang, P. Patras, and H. Haddadi, “Deep learn- http adaptive streaming,” in IEEE Communica- ing in mobile and wireless networking a survey,” in tions Surveys Tutorials 2015, 2015. IEEE COMMUNICATIONS SURVEYS and TU- [26] A. A. Barakabitze, N. Barman, A. Ahmad, S. Zad- TORIALS 2019, 2019. tootaghaj, L. Sun, M. G. Martini, and L. Atzori, [41] K. Arulkumaran, M. P. Deisenroth, M. Brundage, “Qoe management of multimedia streaming ser- and A. A. Bharath, “A brief survey of deep rein- vices in future networks: A tutorial and survey,” forcement learning,” in IEEE SIGNAL PROCESS- in IEEE Communications Surveys Tutorials, 2020, ING MAGAZINE, SPECIAL ISSUE ON DEEP vol. 22, no. 1, 2020, pp. 526–565. LEARNING FOR IMAGE UNDERSTANDING, [27] A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, 2017. and R. Zimmermann, “A survey on bitrate adap- [42] A. Ahmad and L. Atzori, “Mno-ott collaborative tation schemes for streaming media over http,” in video streaming in 5g: The zero-rated qoe approach IEEE Communications Surveys Tutorials, 2019, for quality and resource management,” in IEEE 2019. Transactions on Network and Service Management [28] L. Bertizzolo, E. Demirors, Z. Guan, and T. Melo- 2020, vol. 17, no. 1, 2020, pp. 361–374. dia, “Cobeam: Beamforming-based spectrum shar- [43] T. Huang, C. Zhou, R.-X. Zhang, C. Wu, X. Yao, ing with zero cross-technology signaling for 5g wire- and L. Sun, “Comyco: Quality-aware adaptive less networks,” in IEEE Infocom 2020, 2020. video streaming via imitation learning,” in ACM [29] S. Zheng, T. Han, Y. Jiang, and X. Ge, “Smart MM 2019, 2019. contract-based secure spectrum sharing in multi- [44] B. Han, F. Qian, L. Ji, and V. Gopalakrishnan, operators wireless communication networks,” in “Mp-dash: Adaptive video streaming over ArXiv 2020, 2020. preference-aware multipath,” in Proceedings [30] I. Sodagar, “The mpeg-dash standard for multime- of the 12th International on Conference dia streaming over the internet,” in IEEE Multi- on Emerging Networking EXperiments and media, 2011., 2011. Technologies, ser. CoNEXT ’16. New York, [31] “Apple, “http live streaming.” [online]. available:.” NY, USA: Association for Computing Ma- 20

chinery, 2016, p. 129–143. [Online]. Available: prediction in lte networks,” in IEEE Transactions https://doi.org/10.1145/2999572.2999606 on Mobile Computing, 2018. [45] Z. Song, L. Shangguan, and K. Jamieson, “Wi-fi [56] Y. Qin, S. Hao, K. R. Pattipati, F. Qian, S. Sen, goes to town: Rapid picocell switching for wireless B. Wang, and C. Yue, “Quality-aware strategies for transit networks.” in SIGCOMM 2017, 2017. optimizing abr video streaming qoe and reducing [46] S. Sengupta, N. Ganguly, S. Chakraborty, and data usage,” in Proceedings of the 10th ACM Mul- P. De, “Hotdash: Hotspot aware adaptive video timedia Systems Conference, ser. MMSys ’19, 2019. streaming using deep reinforcement learning,” in [57] C. Ye, M. C. Gursoy, and S. Velipasalar, “Power 2018 IEEE 26th International Conference on Net- control for wireless vbr video streaming: From work Protocols (ICNP), 2018, pp. 165–175. optimization to reinforcement learning,” in IEEE [47] H. Tianchi, Z. Rui-Xiao, Z. Chao, and S. Lifeng, Transactions on Communications, 2019. “Qarc: Video quality aware rate control for real- [58] Z. Meng, J. Chen, Y. Guo, C. Sun, H. Hu, and time video streaming based on deep reinforcement M. Xu, “Pitree: Practical implementation of abr learning,” in Proceedings of the 26th ACM Inter- algorithms using decision trees,” in Proceedings of national Conference on Multimedia, ser. MM ’18, the 27th ACM International Conference on Multi- 2018. media 2019, ser. MM ’19, 2019. [48] B. Rajarshi, B. Archana, R. Desik, R. Mason, [59] J. Molina, D. Muelas, J. E. L. de Vergara, and S. Srinivas, K. Dileep, M. R. K. P., and D. Amogh, J. J. G. Aranda, “Network quality-aware architec- “Qflow: A reinforcement learning approach to high ture for adaptive video streaming from drones,” in qoe video streaming over wireless networks,” in IEEE Internet Computing. 2020, 2020. Mobihoc ’19, 2019. [60] R. Bhattacharyya, A. Bura, D. Rengarajan, M. Ru- [49] H. Yeo, Y. Jung, J. Kim, J. Shin, and D. Han, muly, S. Shakkottai, D. Kalathil, R. K. P. Mok, and “Neural adaptive content-aware internet video de- A. Dhamdhere., “Qflow: A reinforcement learning livery,” in 13th USENIX Symposium on Operating approach to high qoe video streaming over wireless Systems Design and Implementation (OSDI 18), networks.” in ACM Mobihoc 2019, 2019. 2018. [61] K. Bilal and A. Erbad, “Edge computing for inter- [50] D. Raca, A. H. Zahran, C. J. Sreenan, R. K. active media and video streaming,” in 2017 Second Sinha, E. Halepovic, R. Jana, V. Gopalakrishnan, International Conference on Fog and Mobile Edge B. Bathula, and M. Varvello, “Incorporating Computing (FMEC), 2017, pp. 68–73. prediction into adaptive streaming algorithms: A qoe perspective,” in Proceedings of the [62] X. Ran, C. Slocum, M. Gorlatova, and 28th ACM SIGMM Workshop on Network J. Chen, “Sharear: Communication-efficient multi- and Operating Systems Support for Digital user mobile augmented reality,” in Proceedings Audio and Video, ser. NOSSDAV ’18. New of the 18th ACM Workshop on Hot York, NY, USA: Association for Computing Topics in Networks, ser. HotNets ’19. New Machinery, 2018, p. 49–54. [Online]. Available: York, NY, USA: Association for Computing https://doi.org/10.1145/3210445.3210457 Machinery, 2019, p. 109–116. [Online]. Available: [51] G. Baig, J. He, M. A. Qureshi, L. Qiu, G. Chen, https://doi.org/10.1145/3365609.3365867 P. Chen, and Y. Hu, “Jigsaw: Robust live 4k video [63] I. Ben Mustafa, T. Nadeem, and E. Halepovic, streaming,” in The 25th Annual International Con- “Flexstream: Towards flexible adaptive video ference on Mobile Computing and Networking, ser. streaming on end devices using extreme sdn,” MobiCom ’19, 2019. in Proceedings of the 26th ACM International [52] J. Yoon and S. Banerjee, “Hardware-assisted, low- Conference on Multimedia, 2018, ser. MM ’18. cost video transcoding solution in wireless net- New York, NY, USA: Association for Computing works,” in IEEE Transactions on Mobile Comput- Machinery, 2018, p. 555–563. [Online]. Available: ing, 2020, 2020. https://doi.org/10.1145/3240508.3240676 [53] J. Lee, J. Lee, Y. Im, S. Dhawaskar Sathya- [64] M. Liu, F. R. Yu, Y. Teng, V. C. M. Leung, narayana, P. Rahimzadeh, X. Zhang, and M. Song, “Distributed resource allocation in M. Hollingsworth, C. Joe-Wong, D. Grunwald, and blockchain-based video streaming systems with S. Ha, “Castle over the air: Distributed scheduling mobile edge computing,” in IEEE Transactions on for cellular data transmissions,” in Proceedings Wireless Communications, 2019, vol. 18, no. 1, of the 17th Annual International Conference on 2019, pp. 695–708. Mobile Systems, Applications, and Services, ser. [65] T. X. Tran and D. Pompili, “Adaptive bitrate video MobiSys ’19, 2019. caching and processing in mobile-edge computing [54] S. Hu, M. Xu, H. Zhang, C. Xiao, and C. Gui, networks,” in IEEE Transactions on Mobile Com- “Affective content-aware adaptation scheme on qoe puting, 2019, 2019. optimization of adaptive streaming over http,” [66] M. Uddin, T. Nadeem, and S. Nukavarapu, “Ex- in ACM Transactions on Multimedia Computing, treme sdn framework for iot and mobile applica- Communications, and Appli. 2019, 2019. tions flexible privacy at the edge,” in 2019 IEEE [55] C. Yue, R. Jin, K. Suh, Y. Qin, B. Wang, and International Conference on Pervasive Computing W. Wei, “Linkforecast: Cellular link bandwidth and Communications (PerCom, 2019. 21

[67] “Ericsson. 2017. ericsson mobility report. (2017).” [79] J. He, M. A. Qureshi, L. Qiu, J. Li, F. Li, and [Online]. Available: https://goo.gl/mjkwSH. L. Han, “Rubiks: Practical 360-degree streaming [68] W. Hu and G. Cao, “Quality-aware traffic offload- for smartphones,” in Proceedings of the 16th An- ing in wireless networks,” in Proceedings of the nual International Conference on Mobile Systems, 15th ACM International Symposium on Mobile Ad Applications, and Services, 2018. Hoc Networking and Computing, ser. MobiHoc ’14, [80] “2017. h264. (2017).” [Online]. Available: https: 2014, p. 277–286. //www.itu.int/rec/T-REC-H.264 [69] R. S. Kevin Spiteri and D. Sparacio., “From theory [81] “2017. hevc. (2017).” [Online]. Available: https: to practice: Improving bitrate adaptation in the //www.itu.int/rec/T-REC-H.265 dash reference player.” in In Proceedings of the [82] Y. Zhang, P. Zhao, K. Bian, Y. Liu, L. Song, and 9th ACM Multimedia Systems Conference (MMSys X. Li, “Drl360: 360-degree video streaming with 2018)., 2018. deep reinforcement learning,” in IEEE INFOCOM [70] T. Mangla, E. Zegura, M. Ammar, E. Halepovic, 2019 - IEEE Conference on Computer Communi- K.-W. Hwang, R. Jana, and M. Platania, cations, 2019. “Videonoc: Assessing video qoe for network [83] M. Tang and V. W. Wong, “Online bitrate selection operators using passive measurements,” in for viewport adaptive 360-degree video streaming,” Proceedings of the 9th ACM Multimedia in CoRR 2020, 2020. [Online]. Available: https: Systems Conference, ser. MMSys ’18. New //arxiv.org/pdf/2005.02479.pdf York, NY, USA: Association for Computing [84] C. Perfecto, M. S. ElBamby, J. D. Ser, and Machinery, 2018, p. 101–112. [Online]. Available: M. Bennis, “Taming the latency in multi-user https://doi.org/10.1145/3204949.3204956 vr 360-degree: A qoe-aware deep learning-aided [71] T.-Y. Huang, N. Handigol, B. Heller, N. McKeown, multicast framework,” in CoRR, 2020. [Online]. and R. Johari, “Confused, timid, and unstable: Available: http://arxiv.org/abs/1811.07388 Picking a video streaming rate is hard,” in Proceed- [85] S. M. A. Kazmi, T. N. Dang, I. Yaqoob, A. Ndiku- ings of the 2012 Internet Measurement Conference, mana, E. Ahmed, R. Hussain, and C. S. Hong, 2012. “Infotainment enabled smart cars: A joint commu- [72] I. Orsolic, D. Pevec, M. Suznjevic, and L. Skorin- nication, caching, and computation approach,” in Kapov, “Youtube qoe estimation based on the IEEE Transactions on Vehicular Technology 2019., analysis of encrypted network traffic using machine 2019. learning,” in 2016 IEEE Globecom Workshops (GC [86] S. S. H. Khan and M. Bennis, “Enhancing video Wkshps), 2016, pp. 1–6. streaming in vehicular networks via resource slic- [73] Z. W. Zhengfang Duane, Abdul Rehman, “A ing,” in IEEE Transactions on Vehicular Technol- quality-of-experience database for adaptive video ogy. 2019, 2019. streaming,” in IEEE Transactions on Broadcasting [87] Z. Zhang, Y. Yang, Y. H. M. Hua, C. Li, and 2018, 2018. L. Yang, “Proactive caching for vehicular multi- [74] F. Y. Yan, H. Ayers, C. Zhu, and K. Winstein, view 3d video streaming via deep reinforcement “Learning in situ: a randomized experiment in learning,” in IEEE Transactions on Wireless Com- video streaming,” in USENIX NSDI 2020, 2020. munications, 2019, 2019. [75] D. Bhat, A. Rizk, and M. Zink, “Not so quic: A per- [88] R.Ali, B.Kim, S.W.Kim, and H.S.Kim, “(relbt): A formance study of dash over quic,” in Proceedings reinforcement learning-enabled listen before talk of the 27th Workshop on Network and Operating mechanism for lte-laa and wi-fi coexistence in iot,” Systems Support for Digital Audio and Video, ser. in Computer Communications 2020., 2020. NOSSDAV’17, 2017. [89] D. R. C. Li, B. Lin, and M. Pan, “Energy-efficient [76] K. Abdullah, N. Korany, A. Khalafallah, A. Saeed, proactive caching for adaptive video streaming via and A. Gaber, “Characterizing the effects of rapid data-driven optimization,” in IEEE Internet of lte deployment: A data-driven analysis,” in 2019 Things Journal, 2020, 2020. Network Traffic Measurement and Analysis Con- [90] H. Shamim, H. Zhang, C. Moussa, and K. Liu., ference (TMA), 2019, pp. 97–104. “Effective capacity-aware resource allocation for 5g [77] S. Xu, S. Sen, and Z. M. Mao, “Csi: Inferring ultra-dense network with hybrid access mode.” in mobile abr video adaptation behavior under https RACS 2019, 2019. and quic,” in Proceedings of the Fifteenth European [91] C. E. Thornton, R. M. Buehrer, A. F. Martone, and Conference on Computer Systems, ser. EuroSys K. D. Sherbondy, “Experimental analysis of rein- ’20. New York, NY, USA: Association for forcement learning techniques for spectrum sharing Computing Machinery, 2020. [Online]. Available: radar,” in Arxiv 2020, 2020. https://doi.org/10.1145/3342195.3387558 [92] A. S. Melissa Licciardello, Maximilian Grüner, [78] F. Qian, B. Han, Q. Xiao, and V. Gopalakr- “Understanding video streaming algorithms in the ishnan, “Flare: Practical viewport-adaptive 360- wild,” in PAM 2020, 2020. degree video streaming for mobile devices,” in Pro- [93] X. Wang, L. Kong, J. Wu, X. Gao, H. Wang, and ceedings of the 24th Annual International Confer- G. Chen., “Mmhandover: a pre-connection based ence on Mobile Computing and Networking, ser. handover protocol for 5g millimeter wave vehicular MobiCom ’18, 2018. networks.” in ACM IWQoS 2019., 2019. 22

[94] H. Ge, X. Wen, Z. L. W. Zheng, and B. Wang., p. 460–465. [Online]. Available: https://doi.org/ “A history-based hand-dover prediction for lte sys- 10.1145/3204949.3208123 tems.” in International Symposium on Computer- [104] HongziMao, R. Netravali, and M. Alizadeh, “Neu- Network and Multimedia Technology 2009, 2009. ral adaptive video streaming with pensieve,” in [95] I. M. Bălan, B. Sas, T. Jansen, K. S. I. Moer- SIGCOMM ’17, 2017. man, and P. Demeester., “An enhanced weighted [105] M. Z. Xu S., Nikravesh A., “Leveraging context- performance-based handover parameter optimiza- triggered measurements to characterize lte han- tion algorithm for lte networks.” in EURASIP dover performance.” in PAM 2019., 2019. Journal on Wireless Communications andNet- [106] D. Raca, A. H. Zahran, C. J. Sreenan, R. K. working 2011, 2011. Sinha, E. Halepovic, R. Jana, and V. Gopalakrish- [96] W. Luo, M. C. X. Fang, and X. Zhou., “An opti- nan, “On leveraging machine and deep learning for mized handover trigger scheme in lte systems for throughput prediction in cellular networks: Design, high-speed railway.” in In Proceedings of the Fifth performance, and challenges,” in IEEE Communi- In-ternational Workshop on Signal Design and Its cations Magazine, 2020, 2020. Applications in Communications 2018, 2018. [107] X. Chen, T. Tan, and G. Cao, “Energy-aware and [97] D. Raca, A. H. Zahran, C. J. Sreenan, R. K. context-aware video streaming on smartphones,” Sinha, E. Halepovic, R. Jana, V. Gopalakrishnan, in 2019 IEEE 39th International Conference on B. Bathula, and M. Varvello, “Empowering video Distributed Computing Systems (ICDCS), 2019, players in cellular: Throughput prediction from ra- pp. 861–870. dio network measurements,” in ACM MMSys ’19, [108] 2019. Measuring digital development: 2019. Facts and figures 2019. [Online]. Avail- [98] M. Mirza, J. Sommers, P. Barford, and X. Zhu, able: https://www.itu.int/en/ITU-D/Statistics/ “A machine learning approach to tcp throughput Pages/facts/default.aspx. prediction,” in IEEE/ACM Transactions on Net- [109] S. Ahmad, A. L. Haamid, Z. A. Qazi, Z. Zhou, working 2010, vol. 18, no. 4, 2010, pp. 1026–1039. T. Benson, and I. A. Qazi, “A view from the other [99] D. Raca, D. Leahy, C. S. J., and J. J. Quinlan, side: Understanding mobile phone characteristics “Beyond throughput: the next generation a 5g in the developing world,” in Proceedings of the 2016 dataset with channel and context metrics,” in ACM Internet Measurement Conference, ser. IMC ’16. Multimedia Systems Conference, ser. MMSys’ ’20, Association for Computing Machinery, 2016. 2020. [110] T. B. Usama Naseer, “Configtron: Tackling net- [100] N. Baldo, M. Miozzo, M. Requena-Esteso, and work diversity with heterogeneous configurations.” J. Nin-Guerrero, “An open source product-oriented in USENIX HotCloud 2017, 2017. lte network simulator based on ns-3. in proceedings [111] Free Basics Platform. [Online]. Available: https: of the 14th acm international conference on model- //developers.facebook.com/docs/internet-org. ing, analysis and simulation of wireless and mobile [112] A. Tahir, M. T. Munir, S. M. Malik, Z. A. Qazi, systems,” in MSWiM 2011, 2011. and I. A. Qazi, “Deconstructing google’s web light [101] T. Mangla, N. Theera-Ampornpunt, M. Ammar, service,” in Proceedings of The Web Conference E. Zegura, and S. Bagchi, “Video through a crystal 2020, 2020. ball: Effect of bandwidth prediction quality on [113] A. A. Jumani, F. Zafar, Z. A. Qazi, and I. A. Qazi, adaptive streaming in mobile environments,” in “Device-aware adaptive video streaming,” in Pro- Proceedings of the 8th International Workshop ceedings of the ACM SIGCOMM 2019 Conference on Mobile Video, ser. MoVid ’16. New York, Posters and Demos, New York, NY, USA, 2019. NY, USA: Association for Computing Machinery, [114] D. M. Frohlich, D. Rachovides, K. Riga, R. Bhat, 2016. [Online]. Available: https://doi.org/10.1145/ M. Frank, E. A. Edirisinghe, D. Wickramanayaka, 2910018.2910653 M. Jones, and W. Harwood, “Storybank: mobile [102] F. Lu, H. Du, A. Jain, G. M. Voelker, digital storytelling in a development context,” in A. C. Snoeren, and T. andreas, “Cqic: Proceedings of the 27th International Conference Revisiting cross-layer congestion control for on Human Factors in Computing Systems, CHI cellular networks,” in Proceedings of the 16th 2009, Boston, MA, USA, April 4-9, 2009. ACM, International Workshop on Mobile Computing 2009, pp. 1761–1770. Systems and Applications, ser. HotMobile [115] N. Kumar and R. J. Anderson, “Mobile phones for ’15. New York, NY, USA: Association for maternal health in rural india.” in In Proceedings Computing Machinery, 2015. [Online]. Available: of the 33rd Annual ACM Conference on Human https://doi.org/10.1145/2699343.2699345 Factors in Computing Systems., 2015. [103] D. Raca, J. J. Quinlan, A. H. Zahran, and [116] M. Molapo, M. Densmore, and B. DeRenzi, “Video C. J. Sreenan, “Beyond throughput: A 4g lte consumption patterns for first time smartphone dataset with channel and context metrics,” in users: Community health workers in lesotho,” in Proceedings of the 9th ACM Multimedia Systems Proceedings of the 2017 CHI Conference on Human Conference, ser. MMSys ’18. New York, NY, Factors in Computing Systems, ser. CHI ’17. USA: Association for Computing Machinery, 2018, New York, NY, USA: Association for Computing 23

Machinery, 2017, p. 6159–6170. [Online]. Available: Omprakash Gnawali is an As- https://doi.org/10.1145/3025453.3025616 sociate Professor at the Computer [117] J. Pearson, S. Robinson, and M. Jones, “Ex- Science Department of the Univer- ploring low-cost, internet-free information access sity of Houston, USA. He does re- for resource-constrained communities,” in ACM search in wireless networks, cyber- Trans. Comput.-Hum. Interact., 2016. Associa- security, and related technologies tion for Computing Machinery, 2016. and has contributed research arti- [118] “Wi-fi alliance R introduces wi-fi 6.” [Online]. cles, open source software, and in- Available: https://www.wi-fi.org/news-events/ dustry standards. He received his newsroom/wi-fi-alliance-introduces-wi-fi-6 SB and MEng from MIT, PhD [119] S. Li, Y. Huang, C. Li, B. A. Jalaian, Y. T. Hou, from USC, and was a postdoc at and W. Lou., “Coping uncertainty in coexistence Stanford. via exploitation of interference threshold violation.” in ACM Mobihoc 2019, 2019. [120] “Google, “exoplayer.” [online]. available:.” [Online]. Available: https://google.github.io/ ExoPlayer/guide.html [121] “Dashif-reference-player. [online]. available:.” [Online]. Available: http://dashif.org/ reference/players/javascript/1.4.0/samples/ dash-if-reference-player/ [122] “Akamia player testing. [online]. available:.” [Online]. Available: http://players.akamai.com/ players/dashjs [123] “Gpac 2019. gpac multimedia open source project, javascript version of gpac’smp4box tool.” [Online]. Available: https://gpac.github.io/mp4box.js [124] “H. mao, r. netravali, and m alizadeh. 2017. pensieve. code repository.” [Online]. Available: https://github.com/hongzimao/pensieve [125] “Nas. code repository.” [Online]. Available: https: Ahmed Abdelhadi is an Assis- //github.com/kaist-ina/NAS_public tant Professor at the University [126] J. J. Quinlan, A. H. Zahran, and C. J. Sreenan, of Houston. Before joining UH, he “Datasets for avc (h.264) and hevc (h.265) eval- was a Research Assistant Professor uation of dynamic adaptive streaming over http at Virginia Tech. He received his (dash),” in Proceedings of the 7th International Ph.D. in Electrical and Computer Conference on Multimedia Systems, ser. MMSys Engineering from the University of ’16, 2016. Texas at Austin in 2011. He was a member in Wireless Network- ing and Communications Group (WNCG) and Laboratory of Infor- matics, Networks and Communications (LINC) group during his Ph.D. In 2012, he joined Bradley Depart- Kamran Nishat is a Postdoc- ment of Electrical and Computer Engineering and Hume toral Fellow in the Department Center for National Security and Technology at Virginia of Engineering Technology, Uni- Tech. He was a faculty member of Wireless @ Virginia versity of Houston, Texas, USA. Tech. He finished his PhD from Depart- ment of Computer Science, LUMS, Lahore, Pakistan. He has received his B.Sc. degree in Mathematics in 1999 and MCS Computer Science degree, in 2001,from University of Karachi, Karachi. Prior to joining LUMS, he taught in the Department of Computer Science, University of Karachi, Karachi.He received his Masters in computer science in 2007 from LUMS. His research domain is Networking and Systems. He has published in leading Networking conferences including ACM CoNEXT and IEEE Infocom.