Offline Meta-learning for Real-time Bandwidth Estimation

TL;DR

Ivy is a novel offline meta-learning approach for real-time bandwidth estimation in video applications, improving QoE and data efficiency by dynamically selecting optimal algorithms under changing network conditions.

Contribution

The paper introduces Ivy, an offline meta-learning method that enhances real-time bandwidth estimation by adapting to network changes without live data, outperforming existing online methods.

Findings

Ivy improves QoE by up to 11.2% over individual algorithms.

Ivy outperforms existing online meta heuristics by up to 11.4%.

Ivy achieves 21% better QoE with less training data.

Abstract

Real-time video applications require dynamic bitrate adjustments based on network capacity, necessitating accurate bandwidth estimation (BWE). We introduce Ivy, a novel BWE method that leverages offline meta-learning to combat data drift and maximize user Quality of Experience (QoE). Our approach dynamically selects the most suitable BWE algorithm for current network conditions, enabling effective adaptation to changing environments without requiring live network interactions. We implemented our method in Microsoft Teams and demonstrated that Ivy can enhance QoE by 5.9% to 11.2% over individual BWE algorithms and by 6.3% to 11.4% compared to existing online meta heuristics. Additionally, we show that our method is more data efficient compared to online meta-learning methods, achieving up to 21% improvement in QoE while requiring significantly less training data.