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arXiv · 2301.05541

From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning

Xuedou Xiao·Mingxuan Yan·Yingying Zuo·Boxi Liu·Paul Ruan·Yang Cao·Wei Wang
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Abstract

Maximizing quality of experience (QoE) for interactive video streaming has been a long-standing challenge, as its delay-sensitive nature makes it more vulnerable to bandwidth fluctuations. While reinforcement learning (RL) has demonstrated great potential, existing works are either limited by fixed models or require enormous data/time for online adaptation, which struggle to fit time-varying and diverse network states. Driven by these practical concerns, we perform large-scale measurements on WeChat for Business's interactive video service to study real-world network fluctuations. Surprisingly, our analysis shows that, compared to time-varying network metrics, network sequences exhibit noticeable short-term continuity, sufficient for few-shot learning requirements. We thus propose Fiammetta, the first meta-RL-based bitrate adaptation algorithm for interactive video streaming. Building on the short-term continuity, Fiammetta accumulates learning experiences through offline meta-training and enables fast online adaptation to changing network states through a few gradient updates. Moreover, Fiammetta innovatively incorporates a probing mechanism for real-time monitoring of network states, and proposes an adaptive meta-testing mechanism for seamless adaptation. We implement Fiammetta on a testbed whose end-to-end network follows the real-world WeChat for Business traces. The results show that Fiammetta outperforms prior algorithms significantly, improving video bitrate by 3.6%-16.2% without increasing stalling rate.

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Xuedou Xiao, Mingxuan Yan, Yingying Zuo, Boxi Liu, Paul Ruan, Yang Cao, Wei Wang. 2023-01-13. From Ember to Blaze: Swift Interactive Video Adaptation via Meta-Reinforcement Learning. https://arxiv.org/abs/2301.05541

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