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Loki: improving long tail performance of learning-based real-time video adaptation by fusing rule-based models

Published: 25 October 2021 Publication History

Abstract

Maximizing the quality of experience (QoE) for real-time video is a long-standing challenge. Traditional video transport protocols, represented by a few deterministic rules, can hardly adapt to the heterogeneous and highly dynamic modern Internet. Emerging learning-based algorithms have demonstrated potential to meet the challenge. However, our measurement study reveals an alarming long tail performance issue: these algorithms tend to be bottle-necked by occasional catastrophic events due to the built-in exploration mechanisms. In this work, we propose Loki, which improves the robustness of learning-based model by coherently integrating it with a rule-based algorithm. To enable integration at feature level, we first reverse-engineer the rule-based algorithm into an equivalent "black-box" neural network. Then, we design a dual-attention feature fusion mechanism to fuse it with a reinforcement learning model. We train Loki in a commercial real-time video system through online learning, and evaluate it over 101 million video sessions, in comparison to state-of-the-art rule-based and learning-based solutions. The results show that Loki improves not only the average but also the tail performance substantially (26.30% to 44.24% reduction of stall rate and 1.76% to 2.17% increase in video throughput at 95-percentile).

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  1. Loki: improving long tail performance of learning-based real-time video adaptation by fusing rule-based models

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        cover image ACM Conferences
        MobiCom '21: Proceedings of the 27th Annual International Conference on Mobile Computing and Networking
        October 2021
        887 pages
        ISBN:9781450383424
        DOI:10.1145/3447993
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        Published: 25 October 2021

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        Author Tags

        1. hybrid learning
        2. large-scale deployment
        3. real-time video

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        • Youth Top Talent Support Program
        • AIR Program
        • 111 Project
        • NSFC
        • Innovation Research Group Project of NSFC
        • BUPT Excellent Ph.D. Students Foundation

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