Content providers increasingly replace traditional constant bitrate with variable bitrate (VBR) encoding in real-time video communication systems for better video quality. However, VBR encoding often leads to large and frequent bitrate fluctuation, inevitably deteriorating the efficiency of existing adaptive bitrate (ABR) methods. To tackle it, we propose the Anableps to consider the network dynamics and VBR-encoding-induced video bitrate fluctuations jointly for deploying the best ABR policy. With this aim, Anableps uses sender-side information from the past to predict the video bitrate range of upcoming frames. Such bitrate range is then combined with the receiver-side observations to set the proper bitrate target for video encoding using a reinforcement-learning-based ABR model. As revealed by extensive experiments on a real-world trace-driven testbed, our Anableps outperforms the GCC with significant improvement of quality of experience, e.g., 1.88x video quality, 57% less bitrate consumption, 85% less stalling, and 74% shorter interaction delay.

翻译：内容提供商在实时视频通信系统中越来越多地采用可变比特率（VBR）编码替代传统的恒定比特率编码，以获得更好的视频质量。然而，VBR编码常导致比特率大幅且频繁的波动，不可避免地降低了现有自适应比特率（ABR）方法的效率。为解决这一问题，我们提出Anableps方法，联合考虑网络动态与VBR编码引发的视频比特率波动，以部署最优ABR策略。为此，Anableps利用发送端的历史信息预测即将编码帧的视频比特率范围，随后将该比特率范围与接收端的观测结果相结合，通过基于强化学习的ABR模型设置合适的视频编码目标比特率。在实际网络轨迹驱动的测试平台上进行的广泛实验表明，我们的Anableps方法在体验质量方面显著优于GCC方法：视频质量提升1.88倍，比特率消耗减少57%，卡顿次数降低85%，交互延迟缩短74%。