Mowgli：面向实时视频的被动学习速率控制
Mowgli: Passively Learned Rate Control for Real-Time Video
AI总结：
本文提出 Mowgli，仅利用生产环境现有速率控制算法的遥测日志进行被动学习，并通过鲁棒决策技术超越 GCC，显著提升比特率并降低卡顿率。
摘要：
速率控制算法处于视频会议平台的核心，其决定与动态网络特征相匹配的目标比特率，以实现高质量。近期的数据驱动策略已在这一具有挑战性的任务上展现出潜力，但它们在训练期间造成的性能下降对许多生产服务而言是不可接受的障碍，阻碍了采用。本文旨在通过提出另一种经验学习途径来增强数据驱动速率控制的实用性：利用生产环境中现有算法产生的纯已有遥测日志。我们观察到，这些日志包含有效的决策，尽管这些决策往往出现在错误的时间或以错误的顺序出现。尽管基于日志的学习会带来固有不确定性（即缺少针对新决策的反馈），为实现该方法，我们的系统 Mowgli 结合了多种鲁棒学习技术（即保守地推断替代行为以最小化风险，并使用更丰富的模型表述来解释环境噪声）。在多样化网络（仿真和真实世界）中，Mowgli 的表现优于广泛部署的 GCC 算法，将平均视频比特率提升 15–39%，同时将卡顿率降低 60–100%。
英文摘要：
Rate control algorithms are at the heart of video conferencing platforms, determining target bitrates that match dynamic network characteristics for high quality. Recent data-driven strategies have shown promise for this challenging task, but the performance degradation they introduce during training has been a nonstarter for many production services, precluding adoption. This paper aims to bolster the practicality of data-driven rate control by presenting an alternative avenue for experiential learning: leveraging purely existing telemetry logs produced by the incumbent algorithm in production. We observe that these logs contain effective decisions, although often at the wrong times or in the wrong order. To realize this approach despite the inherent uncertainty that log-based learning brings (i.e., lack of feedback for new decisions), our system, Mowgli, combines a variety of robust learning techniques (i.e., conservatively reasoning about alternate behavior to minimize risk and using a richer model formulation to account for environmental noise). Across diverse networks (emulated and real-world), Mowgli outperforms the widely deployed GCC algorithm, increasing average video bitrates by 15-39% while reducing freeze rates by 60-100%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
