Low-latency live streaming (LLS) has emerged as a popular web application, with many platforms adopting real-time protocols such as WebRTC to minimize end-to-end latency. However, we observe a counter-intuitive phenomenon: even when the actual encoded bitrate does not fully utilize the available bandwidth, stalling events remain frequent. This insufficient bandwidth utilization arises from the intrinsic temporal variations of real-time video encoding, which cause conventional packet-level congestion control algorithms to misestimate available bandwidth. When a high-bitrate frame is suddenly produced, sending at the wrong rate can either trigger packet loss or increase queueing delay, resulting in playback stalls. To address these issues, we present Camel, a novel frame-level congestion control algorithm (CCA) tailored for LLS. Our insight is to use frame-level network feedback to capture the true network capacity, immune to the irregular sending pattern caused by encoding. Camel comprises three key modules: the Bandwidth and Delay Estimator and the Congestion Detector, which jointly determine the average sending rate, and the Bursting Length Controller, which governs the emission pattern to prevent packet loss. We evaluate Camel on both large-scale real-world deployments and controlled simulations. In the real-world platform with 250M users and 2B sessions across 150+ countries, Camel achieves up to a 70.8% increase in 1080P resolution ratio, a 14.4% increase in media bitrate, and up to a 14.1% reduction in stalling ratio. In simulations under undershooting, shallow buffers, and network jitter, Camel outperforms existing congestion control algorithms, with up to 19.8% higher bitrate, 93.0% lower stalling ratio, and 23.9% improvement in bandwidth estimation accuracy.

翻译：低延迟直播已成为一种流行的网络应用，众多平台采用如WebRTC等实时协议以最小化端到端延迟。然而，我们观察到一个反直觉的现象：即使实际编码码率未充分利用可用带宽，卡顿事件仍频繁发生。这种带宽利用不足源于实时视频编码固有的时间波动性，导致传统包级拥塞控制算法错误估计可用带宽。当突然产生高码率帧时，以错误速率发送数据可能引发丢包或增加排队延迟，从而导致播放卡顿。为解决这些问题，我们提出了Camel——一种专为低延迟直播设计的创新帧级拥塞控制算法。我们的核心思路是利用帧级网络反馈来捕获真实的网络容量，从而免疫由编码引起的不规则发送模式。Camel包含三个关键模块：带宽与延迟估计器、拥塞检测器（共同确定平均发送速率）以及突发长度控制器（用于调控发送模式以防止丢包）。我们在大规模实际部署和受控仿真环境中对Camel进行了评估。在覆盖150多个国家、拥有2.5亿用户和20亿会话的真实平台中，Camel实现了1080P分辨率占比最高70.8%的提升，媒体码率提升14.4%，卡顿率最高降低14.1%。在欠利用、浅缓冲区及网络抖动的仿真场景下，Camel优于现有拥塞控制算法，码率最高提升19.8%，卡顿率降低93.0%，带宽估计准确度提高23.9%。