CS2P: Improving Video Bitrate Selection and Adaptation with Data-Driven Throughput Prediction

By Yi Sun, Xiaoqi Yin, Junchen Jiang, Vyas Sekar, Fuyuan Lin, Nanshu Wang, Tao Liu, Bruno Sinopoli. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2016

434 citations recorded by the source corpus.

Bitrate adaptation is critical in ensuring good users’ quality-of-experience (QoE) in Internet video delivery system. Several efforts have argued that accurate throughput prediction can dramatically improve (1) initial bitrate selection for low startup delay and high initial resolution; (2) midstream bitrate adaptation for high QoE. However, prior ef- forts did not systematically quantify real-world throughput predictability or develop good prediction algorithms. To bridge this gap, this paper makes three key technical contributions: First, we analyze the throughput characteristics in a dataset with 20M+ sessions. We find: (a) Sessions sharing similar key features (e.g., ISP, region) present similar initial values and dynamical patterns; (b) There is a natural “stateful” dynamical behavior within a given session. Second, building on these insights, we develop CS2P, a better throughput prediction system. CS2P leverages data-driven approach to learn (a) clusters of similar sessions, (b) an initial throughput predictor, and (c) a Hidden-Markov-Model based midstream predictor modeling the stateful evolution of throughput. Third, we develop a prototype system and show by trace-driven simulation and real-world experiments that CS2P outperforms state-of-art by 40% and 50% median pre- diction error respectively for initial and midstream through- put and improves QoE by 14% over buffer-based adaptation algorithm.

Subfields: Computer Networks and Communications, Hardware and Architecture, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/65f4797381067e47481decb546879f6b656d14b9

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Citations in the corpus

Resource Management with Deep Reinforcement Learning (2016, background): “[39], and operate well for different codecs, screen sizes and available bandwidths (e.g., DSL vs. T1).”
Reinforcement Learning for Primary care e Appointment Scheduling (2017, background): “The algorithm usually has to decide on noisy input or incomplete information [4]; 3.”
Oboe: auto-tuning video ABR algorithms to network conditions (2018, background): “While the magnitude of benefits is smaller with the ideal prediction approach, in practice Oboe will likely result in larger benefits, since even more sophisticated schemes [49] cannot achieve the ideal predictions, and the errors are likely to grow with larger T.”
Machine Learning for Networking: Workflow, Advances and Opportunities (2017, methodology): “First, as the best known capabilities of ML, classification and prediction play basic but important roles in network problems such as intrusion detection and performance prediction [1].”
Learning in situ: a randomized experiment in video streaming (2019, methodology): “On a cold start to a new session, prior work [19,40] suggested a need for session clustering to determine the quality of the ﬁrst chunk.”
Neural Adaptive Video Streaming with Pensieve (2017, background): “However, selecting the right bitrate can be very challenging due to (1) the variability of network throughput [18, 41, 48, 51, 52]; (2) the conflicting video QoE requirements (high bitrate, minimal rebuffering, smoothness, etc.”
A Survey on Bitrate Adaptation Schemes for Streaming Media Over HTTP (2019, background): “All theses studies as well as C3 [156], CFA [157], CS2P [158], Pytheas [159] share a common characteristic, which is that there exists a central controller to control, manage and monitor HAS traffic.”