When BBR Meets Live Streaming
This work addresses a practical problem for live-streaming applications (e.g., TikTok Live) by improving BBR's adaptation to such scenarios, but the solution is incremental.
BBR, originally designed for bulk data transmission, suffers from inaccurate bandwidth estimation in live streaming, causing startup-phase losses and underutilization. BBR-Copilot, an auxiliary component that generates accurate bandwidth samples by sending extra data, improves BBR's performance in live-streaming scenarios.
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.