Ph.D. Defense: Yanyuan Qin

Title: Adaptive Bitrate Streaming Over Cellular Networks: Rate Adaptation and Data Savings Strategies

Student: Yanyuan Qin
Major Advisor: Dr. Bing Wang
Associate Advisors: Dr. Song Han, Dr. Krishna R. Pattipati, Dr. Subhabrata Sen (AT&T Labs Research)

Date/Time: Wednesday, November 4^th, 2020, 10am – 12pm
Location: 

Meeting link:  https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=maefbffb356828c28bb8907fe3c3e6dcd

Password: rvMwNCvg358

Join by phone: +1-415-655-0002 US Toll

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Abstract: 

Adaptive bitrate streaming (ABR) has become the de facto technique for video streaming over the Internet. Despite a flurry of techniques, achieving high quality ABR streaming over cellular networks remains a tremendous challenge.  First, the design of an ABR scheme needs to balance conflicting Quality of Experience (QoE) metrics such as video quality, quality changes, stalls and startup performance, which is even harder under highly dynamic bandwidth in cellular network. Second, streaming providers have been moving towards using Variable Bitrate (VBR) encodings for the video content, which introduces new challenges for ABR streaming, whose nature and implications are little understood. Third, mobile video streaming consumes a lot of data. Although many video and network providers currently offer data saving options, the existing practices are suboptimal in QoE and resource usage. Last, when the audio and video tracks are stored separately, video and audio rate adaptation needs to be dynamically coordinated to achieve good overall streaming experience, which presents interesting challenges while, somewhat surprisingly, has received little attention by the research community. In this dissertation, we tackle each of the above four challenges.

Firstly, we design a framework called PIA (PID-control based ABR streaming) that strategically leverages PID control concepts and novel approaches to account for the various requirements of ABR streaming. The evaluation results demonstrate that PIA outperforms state-of-the-art schemes in providing high average bitrate with significantly lower bitrate changes and stalls, while incurring very small runtime overhead. We further design PIA-E (PIA Enhanced), which improves the performance of PIA in the important initial playback phase.

Secondly, we identify distinguishing characteristics of VBR encodings that impact user QoE and should be factored in any ABR adaptation decision and find that traditional ABR adaptation strategies designed for the Constant Bitrate (CBR) encodings are not adequate for VBR. We develop novel best practice design principles to guide ABR rate adaptation for VBR encodings. As a proof of concept, we design a novel and practical control-theoretic rate adaptation scheme, CAVA (Control-theoretic Adaption for VBR-based ABR streaming), incorporating these concepts. Extensive evaluations show that CAVA substantially outperforms existing state-of-the-art adaptation techniques.

Thirdly, we analyze the underlying causes for suboptimal existing data saving practices and propose novel approaches to achieve better tradeoffs between video quality and data usage. The first approach is Chunk-Based Filtering (CBF), which can be retrofitted to any existing ABR scheme. The second approach is QUality-Aware Data-efficient streaming (QUAD), a holistic rate adaptation algorithm that is designed ground up. Our evaluations demonstrate that compared to the state of the art, the two proposed schemes achieve consistent video quality that is much closer to the user-specified target, lead to far more efficient data usage, and incur lower stalls.

For the fourth challenge, we first examine the state of the art in the handling of demuxed audio and video tracks in predominant ABR protocols (DASH and HLS), as well as in real ABR client implementations in three popular players covering both browsers and mobile platforms. Combining experimental insights with code analysis, we shed light on a number of limitations in existing practices both in the protocols and the player implementations, which can cause undesirable behaviors such as stalls, selection of potentially undesirable combinations such as very low quality video with very high quality audio, etc. Based on our gained insights, we identify the underlying root causes of these issues, and propose a number of practical design best practices and principles  whose collective adoption will help avoid these issues and  lead to better QoE.