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Joint Buffering and Rate Control for Video Streaming Over Heterogeneous Wireless Networks by Lei Hua a Thesis Submitted in Confo Joint Buffering and Rate Control for Video Streaming over Heterogeneous Wireless Networks by Lei Hua A thesis submitted in conformity with the requirements for the degree of Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto Copyright c 2010 by Lei Hua Abstract Joint Buffering and Rate Control for Video Streaming over Heterogeneous Wireless Networks Lei Hua Master of Applied Science Graduate Department of Electrical and Computer Engineering University of Toronto 2010 The integration of heterogeneous access networks is becoming a possible feature of 4G wireless networks. It is challenging to deliver the multimedia services over such integrated networks because of the discrepancy in the bandwidth of different networks. This thesis presents an adaptive approach that combines source rate adaptation and buffering to achieve high quality VBR video streaming with less quality variation over an integrated two-tier network. Statistical information of the residence time in each network or local- ization information are utilized to anticipate the handoff occurrence. The performance of this approach is analyzed under the CBR case using a Markov reward model. Simulation under the CBR and VBR cases is conducted for different types of network models. The results are compared with a dynamic programming algorithm as well as other naive or intuitive algorithms, and proved to be promising. ii Acknowledgements I would like to express my sincerest gratitude to my supervisor, Professor Ben Liang, for this exciting opportunity to work under his supervision at this prestigious institu- tion. During the whole process he provided me with invaluable guidance, inspiration and support, without which I couldn’t have completed this work. I am thankful to the members of my thesis committee, Prof. Elvino S. Sousa, Prof. Raviraj Adve, and Prof. Jason H. Anderson for the time spent in reviewing my thesis, and for their helpful feedback and comments on improving its content. I thank all my current and former colleagues in my research group for their useful inputs and suggestions on the research work itself and also the presentation of the work. Special thanks to all of my friends at University of Toronto, Colin Jiang, Eric Yuan, Junqi Yu, Lilin Zhang, Weiwei Li, Yuan Feng, Yunfeng Lin and others, whose company, care and encouragement made the two years of Master’s studies much more enjoyable. Last but never the least, I dedicate this thesis to my family, who are always there for me in my life. iii Contents 1 Introduction 1 1.1 Overview.................................... 1 1.1.1 VideoStreaming ........................... 1 1.1.2 Heterogeneous Wireless Networks . 2 1.1.3 Buffering................................ 3 1.1.4 RateAdaptation ........................... 4 1.1.5 ContributionoftheThesis . 4 1.2 ThesisOutline................................. 6 2 Literature Review 7 2.1 VideoRateAdaptationTechniques . 7 2.1.1 Transcoding.............................. 8 2.1.2 Joint Source/Channel Coding . 8 2.1.3 ScalableVideoCoding ........................ 9 2.1.4 Content-Aware Coding Techniques . 10 2.2 Rate Control in Heterogeneous Wireless Networks . 11 2.3 Buffering in Heterogeneous Wireless Networks . 12 3 Problem Statement 14 3.1 ApplicationScenario ............................. 14 3.2 ModelsandAssumptions........................... 16 iv 3.2.1 Rate Adaptation and Playback . 16 3.2.2 Residence Time and Rate Estimation . 18 3.2.3 Feedback Control Mechanism . 19 3.3 ProblemFormulation............................. 19 4 Generic Network Model 22 4.1 ControlAlgorithms .............................. 22 4.1.1 Adaptive Control Algorithm . 22 4.1.2 SimpleAlgorithm........................... 25 4.1.3 Mean Residual Life Based Algorithm . 26 4.1.4 SimpleShapingAlgorithm . 27 4.2 Analytical Framework and Analytical Results . 29 4.2.1 Analytical Results for Generic Model . 30 5 Markov Chain Network Model 36 5.1 MarkovDecisionProcessModel . 36 5.2 DynamicProgrammingAlgorithm. 38 5.3 SimulationResults .............................. 39 5.4 MoreRealistic3-ZoneNetworkModel. 43 5.5 PH-FittingofResidenceTimes . 45 5.6 Estimation in Adaptive Control Algorithm . 47 5.6.1 Utilizing Statistical Information . 47 5.6.2 Utilizing Localization Information . 48 5.6.3 Simulation Results for 3-Zone Model . 49 5.7 Simulating with VBR Network and VBR Video Stream . 52 6 Conclusion 57 Bibliography 59 v List of Tables 3.1 Notationsinsystemmodel.......................... 20 4.1 Analysisparameters-1............................ 31 4.2 Analysisparameters-2............................ 31 5.1 Simulation parameters for 2-zone Markov model . ...... 40 5.2 Simulation parameters for 3-zone model . ..... 49 5.3 Simulation parameters for VBR network and VBR video . 52 vi List of Figures 3.1 Integrated two-tier network . 15 3.2 Relationship between the transmission sequence in time and the playback sequenceintime ............................... 18 4.1 Illustration of proportional feedback controller . .......... 24 4.2 Distributionsofresidencetimes . 32 4.3 Analysis vs simulation results: generic model, Gamma distribution - 1 . 34 4.4 Analysis vs simulation results: generic model, Gamma distribution - 2 . 35 5.1 DP: variation and utilization vs. α ..................... 41 5.2 Adaptive algorithm: variation and utilization vs. β ............ 41 5.3 Comparison between algorithms: utilization vs. variation ......... 42 5.4 Integrated two-tier network with 2-zone T2N . ...... 44 5.5 An example of the generated user’s moving trace . 44 5.6 CDF’s of residence times in different zones . 46 5.7 PH-fittedMarkovchainnetworkmodel . 47 5.8 DP on 3-zone model: variation and utilization vs. α ............ 50 5.9 Adaptive algorithm on 3-zone model: variation and utilization vs. β . 51 5.10 Comparison DP and AA: utilization vs. variation . 51 5.11 VBR simulation: adaptive algorithm with statistical information . 53 5.12 VBR simulation: adaptive algorithm with localization information . 53 vii 5.13 VBR simulation: simple adaptive algorithm . 54 5.14 Simulating VBR case - variation vs. β ................... 55 5.15 Simulating VBR case - utilization vs. β ................... 55 5.16 Simulating VBR case - utilization vs. variation . ...... 56 viii Chapter 1 Introduction 1.1 Overview 1.1.1 Video Streaming Online video has become a mainstream medium and the single most influential factor driving the need for increased mobile network capacity [8]. It would take 28 years to watch the video uploaded to YouTube in the week of April 29th, 2010 [20]; HD(high defi- nition) movies and television programs are widely available online with the help of CDNs (Content Distribution Networks) and P2P (Peer-to-Peer) networks; video conferencing and video phones are not the exclusive rights of large companies any more, but can be enjoyed by individuals and families. It is then important and interesting to research on improving video streaming techniques. There are two types of video streaming applications: live streaming, which captures real-time events and provides the video to users, and on-demand streaming, which offers stored video contents. Application scenarios of live streaming include video conference, video phone and live event broadcasting, which have stringent delay requirement. In this thesis we consider the transmission of pre-encoded video, which is used for delivering all kinds of published video contents and user generated contents online and is expected to 1 Chapter 1. Introduction 2 account for sixty-six percent of the world’s mobile data traffic by 2014 [21]. In comparison to other traffic flows such as Web browsing and E-mail, video streaming has its unique characteristics and therefore may impose certain requirements on the network. Video streaming traffic is inelastic. Unlike web browsing or file downloading, where data can be transmitted at any rate, video streaming requires certain amount of data to be delivered and decoded before the playback deadline. Hence it is sensitive to variations in both bandwidth and transmission delay. Video streaming applications are loss-tolerant. Robust coding techniques allow video to be decoded with certain loss of data. However, this does not mean any level of loss can be tolerated. In high error- rate networks, it is challenging to develop loss-prevention techniques for robust video transmission. 1.1.2 Heterogeneous Wireless Networks With the rapid growth of mobile communication technology, various wireless networking technologies have evolved and become widely deployed all over the world, allowing people to access the Internet with all kinds of mobile computing devices, at all times and all places. The popular access technologies include IEEE802.11 wireless local area networks (WLAN), WiMAX, GPRS, UMTS, and CDMA2000, etc. These technologies are hetero- geneous in certain attributes, such as coverage area, protocol, signaling mechanism, data rate, error rate, etc. However, it is common for the personal mobile devices (laptops, smart phones, PDAs, digital media players) to support more than one wireless access technologies simultaneously. With the coexistence of heterogeneous wireless networks and the devices supporting multiple access technologies, the integration of heterogeneous wireless networks is be- coming a trend and is part of the 4G network design [30]. This feature allows user to seamlessly switch among different wireless network interfaces and enjoy greatly enlarged coverage and more reliable wireless access on a single device. Chapter 1. Introduction
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