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CSI: Inferring Mobile ABR Video Adaptation Behavior Under HTTPS and QUIC CSI: Inferring Mobile ABR Video Adaptation Behavior under HTTPS and QUIC Shichang Xu Subhabrata Sen Z. Morley Mao University of Michigan AT&T Labs – Research University of Michigan Abstract Server Manifest Network Client Mobile video streaming services have widely adopted Adap- Chunks HTTP tive Bitrate (ABR) streaming to dynamically adapt the stream- Track ing quality to variable network conditions. A wide range of 720p 1 Buffer third-party entities such as network providers and testing 480p IP packets services need to understand such adaptation behavior for 360p 1 2 3 Index purposes such as QoE monitoring and network management. CSI The traditional approach involved conducting test runs and analyzing the HTTP-level information from the associated network traffic to understand the adaptation behavior under Figure 1. ABR streaming overview different network conditions. However, end-to-end traffic encryption protocols such as HTTPS and QUIC are being increasingly used by streaming services, hindering such tra- Rate (ABR) streaming (predominantly HLS [75] and DASH [31]) ditional traffic analysis approaches. has been widely adopted in industry for delivering satisfac- To address this, we develop CSI (Chunk Sequence Infer- tory Quality of Experience (QoE) over dynamic cellular net- encer), a general system that enables third-parties to conduct work conditions. The server encodes each video into multiple active measurements and infer mobile ABR video adapta- versions with different picture quality levels and encoding tion behavior based on packet size and timing information bitrates (with higher bitrates for higher-quality encodings) still available in the encrypted traffic. We perform exten- called tracks, and splits each track into shorter chunks, each sive evaluations and demonstrate that CSI achieves high representing a few seconds worth of playback content (Fig- inference accuracy for video encodings of popular streaming ure 1). During streaming, the client downloads a metadata services covering various ABR system designs. As an illus- file (called manifest) from the server which contains infor- tration, for a popular mobile video service, we show that CSI mation about all the tracks and their corresponding chunks. can effectively help understand the video QoE implications Then it downloads (using HTTP) and plays individual chunks of network traffic shaping policies and develop optimized in order of increasing playback indexes (ie., playback posi- policies, even in the presence of encryption. tion in the video). The streaming is adaptive to time-varying ACM Reference Format: network conditions – for each playback index, the client dy- Shichang Xu, Subhabrata Sen, and Z. Morley Mao. 2020. CSI: In- namically selects one among the “ladder" of available tracks ferring Mobile ABR Video Adaptation Behavior under HTTPS and for download, based on prevailing network conditions and QUIC. In Fifteenth European Conference on Computer Systems (Eu- custom complex adaptation algorithms [56, 58, 69, 76, 84]. roSys ’20), April 27–30, 2020, Heraklion, Greece. ACM, New York, NY, The ABR adaptation logic needs to deal with time-varying USA, 16 pages. https://doi.org/10.1145/3342195.3387558 network conditions and make complex tradeoffs between (sometimes) competing QoE requirements (e.g., stream high- 1 Introduction quality video tracks/chunks that require high network band- Mobile video streaming is increasingly popular, already ac- widths, but still minimize stalls that can occur when the counting for 60% of mobile data traffic, and is predicted network bandwidth drops), and there is no single or simple to grow to 78% by 2021 [40]. HTTP-based Adaptive Bit “optimal” adaptation logic design. Different mobile streaming systems adopt different ABR strategies and tradeoffs. Their Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are clients exhibit substantially different adaptation behaviors not made or distributed for profit or commercial advantage and that copies even under the same network conditions, and they keep bear this notice and the full citation on the first page. Copyrights for third- evolving over time [52, 80, 83]. party components of this work must be honored. For all other uses, contact In this work, we develop a novel and general system, CSI the owner/author(s). (Chunk Sequence Inferencer), that provides the capability to EuroSys ’20, April 27–30, 2020, Heraklion, Greece independently conduct active measurements and infer the adap- © 2020 Copyright held by the owner/author(s). ACM ISBN 978-1-4503-6882-7/20/04. tation behavior and delivered QoE of third party mobile video https://doi.org/10.1145/3342195.3387558 services, for the increasingly common but challenging use case EuroSys ’20, April 27–30, 2020, Heraklion, Greece Shichang Xu, Subhabrata Sen, and Z. Morley Mao where these services use encrypted (HTTPS/QUIC) communica- To address this challenge, CSI works by inferring the iden- tions between the client and the server. Such video streaming tities of downloaded chunks from the encrypted network systems are typically complex, highly customized and closed traffic (§3). It leverages the key insight that for commonly source, making it challenging to understand their adaptation used TLS traffic encryption, the data volume sent over the designs. To address this, for a specific streaming service and network is similar to the corresponding object size before video asset, CSI streams the video under specific network encryption. Before running the active measurement, CSI ob- conditions of interest (§4). It then analyzes the associated tains the sizes of all chunks across all tracks for the target network traffic to infer (1) the identity of each downloaded test video. After running the video streaming session, it ana- chunk, i.e., the index, the track it belongs to, whether it is an lyzes the IP-level information still available in the encrypted audio or video chunk and (2) the time when each chunk is traffic - specifically the packet sizes and timing information. downloaded. From such information, QoE metrics including Conceptually, it analyzes the encrypted traffic to first infer displayed video quality and stall occurrences can be further the packets corresponding to client requests for chunks, and analyzed. then estimates the size of each downloaded chunk based CSI should be particularly useful to a wide range of third- on the traffic downloaded between consecutive requests. It party entities who desire to independently evaluate and un- then uses the chunk size as a fingerprint to identify the cor- derstand the adaptation behavior of popular mobile video responding playback index and track for each downloaded services. Examples include: (1) mobile network providers chunk. desiring to understand whether popular video services are Our key contributions are: able to deliver satisfactory QoE over their network, and to inform the design of traffic shaping policies that can support delivering good QoE while making efficient use of network • Foundational insights (§3). We perform extensive mea- resources. (2) video services desiring to conduct comparative surements and develop two key insights that demon- analysis of the QoE performance of their service with other strate the feasibility of inferring chunk identities from video services for calibration purposes and to understand encrypted traffic. (1) Downloaded object sizes canbe how to improve their adaptation design , and (3) independent accurately inferred from associated encrypted packets testing services and researchers interested in profiling the (§3.2). (2) For the increasingly commonly used Vari- adaptation behavior of popular video services, to identify able Bitrate (VBR) encoding, even with a relatively potential deficiencies and drive better designs. A common short sequence of chunk sizes, consisting of a mixture approach in these cases is to perform active measurements: of chunks from different tracks, the identity of each testers stream videos on the target ABR service in certain chunk in the sequence can still be identified with high network conditions (either in the lab or in the wild). They accuracy (§3.3). collect and analyze the network traffic trace generated from • Design of CSI (§4). CSI enables automated and re- the testing to study player behavior dynamics and charac- peated active measurements for understanding the terize delivered QoE. Without a tool like CSI, it is extremely adaptation behavior and delivered QoE of commer- challenging to conduct such measurements and study the cial mobile video streaming under various network adaptation behavior of commercial streaming services from conditions. CSI automates the measurement process encrypted network traffic. including performing network emulation, player UI One key challenge CSI addresses is that popular streaming instrumentation, data collection and analysis. apps [34, 36] are increasingly adopting end-to-end encryp- • Inference algorithm that is a key component of CSI (§5). tion protocols like HTTPS and QUIC and encrypt packet pay- It is designed to cover a range of common ABR system loads. Existing active measurement approaches depend on designs. To efficiently identify the chunk sequence being able to extract application level information from the that matches size information from the traffic, CSI network traffic between the client and server, and determine formulates the matching problem as a shortest path the
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