Real-time video streaming relies on rate control mechanisms to adapt video bitrate to network capacity while maintaining high utilization and low delay. However, the current video rate controllers, such as Google Congestion Control (GCC) in WebRTC, are very slow to respond to network changes, leading to link under-utilization and latency spikes. While recent delay-based congestion control algorithms promise high efficiency and rapid adaptation to variable conditions, low-latency video applications have been unable to adopt these schemes due to the intertwined relationship between video encoders and rate control in current systems. This paper introduces Vidaptive, a new rate control mechanism designed for low-latency video applications. Vidaptive decouples packet transmission decisions from encoder output, injecting dummy padding traffic as needed to treat video streams akin to backlogged flows controlled by a delay-based congestion controller. Vidaptive then adapts the frame rate, resolution, and target bitrate of the encoder to align the video bitrate with the congestion controller's sending rate. Our evaluations atop WebRTC show that, across a set of cellular traces, Vidaptive achieves ~2x higher video bitrate and 1.6 dB higher PSNR, and it reduces 95th-percentile frame latency by 2.7s with a slight increase in median frame latency.

翻译：摘要：实时视频流依赖率控机制将视频码率适配至网络容量，同时保持高利用率与低延迟。然而，当前视频率控制器（如WebRTC中的Google拥塞控制算法GCC）对网络变化的响应极其缓慢，导致链路利用率不足和延迟尖峰。尽管基于延迟的新型拥塞控制算法承诺在高效率与快速适应可变条件方面具有优势，但由于当前系统中视频编码器与率控机制相互耦合，低延迟视频应用始终无法采用这些方案。本文提出Vidaptive——一种专为低延迟视频应用设计的新型率控机制。Vidaptive将数据包传输决策与编码器输出解耦，按需注入虚拟填充流量，从而使视频流可被视同为由延迟型拥塞控制器管理的积压数据流。随后，Vidaptive调整编码器的帧率、分辨率与目标码率，使视频比特率与拥塞控制器的发送速率对齐。我们在WebRTC平台上开展的评估表明：在多个蜂窝网络轨迹测试中，Vidaptive的视频码率提升约2倍，峰值信噪比提高1.6 dB，并将第95百分位帧延迟降低2.7秒，仅中位帧延迟略有增加。