Dynamic Pacing for Real-time Satellite Traffic

TL;DR

This paper proposes a data-driven queue management method for real-time satellite video communication that adapts to handover activity, significantly improving video quality and reducing freezes in LEO satellite networks.

Contribution

It introduces a novel queue management mechanism that dynamically adjusts pacing based on predicted handover activity, enhancing performance over existing WebRTC policies.

Findings

Up to 3x increase in video bitrate

62% reduction in freeze rate in emulation

40% decrease in packet loss on real Starlink networks

Abstract

Google's congestion control (GCC) has become a cornerstone for real-time video and audio communication, yet its performance remains fragile in emerging Low Earth Orbit (LEO) networks. In this paper, we study the behavior of videoconferencing systems in LEO constellations. We observe that video quality degrades due to inherent delays and network instability introduced by the high altitude and rapid movement of LEO satellites, with these effects exacerbated by WebRTC's conventional "one-size-fits-all" sender-side pacing queue management. To address these challenges, we introduce a data-driven queue management mechanism that tunes the maximum pacing queue capacity based on predicted handover activity, minimizing latency during no-handover periods and prioritizing stability when entering periods of increased handover activity. Our method yields up to 3x improvements in video bitrate and reduces freeze rate by 62% in emulation, while delivering up to a 41% reduction in freeze rate and 40% decrease in mean packet loss on real Starlink constellations compared to WebRTC's default pacing queue policy.