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This paper addresses the limitations of the BBR congestion control algorithm when applied to live streaming, specifically its struggles with inaccurate bandwidth estimation that lead to inefficient data transmission. The authors introduce BBR-Copilot, an auxiliary component that enhances BBR's adaptability by generating accurate bandwidth measurements through the strategic transmission of additional data. Experimental results demonstrate that BBR-Copilot significantly improves streaming performance, reducing self-inflicted losses and optimizing data rates in live scenarios.
BBR-Copilot transforms BBR from a bulk data algorithm into a robust solution for live streaming, overcoming its critical bandwidth estimation flaws.
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.
