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.

翻译：最近，亚马逊、腾讯、字节跳动和华为等行业先驱已开始采用BBR作为其直播流媒体应用（包括抖音直播）的拥塞控制算法。然而，BBR最初是为批量数据传输而设计的，在面对直播流媒体场景时面临多重挑战。本文首先探讨了直播场景中因带宽估计不准确导致BBR存在的两个关键问题：(i) BBR难以退出启动阶段，造成严重的自激丢包；(ii) 在稳定阶段，BBR以低于可用带宽的速率发送数据。随后我们提出BBR-Copilot——一种辅助拥塞控制组件，通过与BBR协同工作，使BBR更好地适应直播流媒体场景。BBR-Copilot能通过智能创建和发送额外数据来主动生成准确的带宽测量样本。我们在QUIC协议上实现了BBR-Copilot原型并通过测试平台进行验证。实验评估结果表明，BBR-Copilot能有效提升BBR在直播流媒体场景中的性能。