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arXiv · 2606.03468

When BBR Meets Live Streaming

Xu Yan·Tong Li·Bo Wu·Cheng Luo·Jiuxiang Zhu·Laizhong Cui
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Abstract

Recently, industrial pioneers like Amazon, Tencent, ByteDance, and Huawei have been adopting BBR as their congestion control algorithm for live-streaming applications, including TikTok Live. However, BBR, originally crafted for bulk data transmission, faces multiple challenges in live-streaming scenarios. In this paper, we first explore two key issues associated with BBR due to inaccurate bandwidth estimation in live-streaming scenarios: (i) BBR cannot easily exit its startup phase, resulting in a fierce self-inflicted loss. (ii) BBR sends data at a lower rate than the available bandwidth during its stable phase. We then propose BBR-Copilot, an auxiliary congestion control component that cooperates with BBR, making BBR better adapt to live-streaming scenarios. BBR-Copilot allows for proactively generating accurate bandwidth measurement samples by smartly creating and sending extra data. We implement the BBR-Copilot prototype upon QUIC and evaluate it via testbed. Experimental evaluation results show that BBR-Copilot effectively enhances BBR's performance in live-streaming scenarios.

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Xu Yan, Tong Li, Bo Wu, Cheng Luo, Jiuxiang Zhu, Laizhong Cui. 2026-06-02. When BBR Meets Live Streaming. https://arxiv.org/abs/2606.03468

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