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), 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 target bitrate of the encoder based on delay measurements to align the video bitrate with the congestion controller's sending rate. Our evaluations atop Google's implementation of WebRTC show that, across a set of cellular traces, Vidaptive achieves ~1.5x higher video bitrate and 1.4 dB higher SSIM, 1.3 dB higher PSNR, and 40% higher VMAF, and it reduces 95th-percentile frame latency by 2.2 s with a slight 17 ms increase in median frame latency.

翻译：实时视频流依赖速率控制机制在保持高利用率与低延迟的同时，使视频比特率适配网络容量。然而，当前视频速率控制器（如Google拥塞控制GCC）对网络变化的响应极为缓慢，导致链路利用率不足与延迟尖峰。尽管近期基于延迟的拥塞控制算法承诺在可变条件下实现高效与快速自适应，但由于现有系统中视频编码器与速率控制的耦合关系，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全新速率控制机制。Vidaptive将数据包发送决策与编码器输出解耦，在必要时注入"哑"填充流量，使视频流如同由基于延迟的拥塞控制器管理的积压流量。随后，Vidaptive通过延迟测量自适应调整编码器的目标比特率，使视频比特率与拥塞控制器的发送速率对齐。我们在Google WebRTC实现上的评估表明：在蜂窝网络轨迹数据集上，Vidaptive实现了约1.5倍的视频比特率提升、1.4 dB的SSIM增益、1.3 dB的PSNR增益以及40%的VMAF提升，同时将第95百分位帧延迟降低2.2秒，中位帧延迟仅增加17毫秒。