Mowgli: Passively Learned Rate Control for Real-Time Video

TL;DR

Mowgli is a passive, data-driven rate control system for real-time video that learns from existing telemetry logs, improving video quality and reducing freezes without the training performance issues of prior methods.

Contribution

This paper introduces Mowgli, a novel passive learning approach that leverages existing telemetry logs for effective rate control in video conferencing.

Findings

Increases average video bitrates by 15-39%.

Reduces freeze rates by 60-100%.

Outperforms the widely used GCC algorithm.

Abstract

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%.