Offline Meta-learning for Real-time Bandwidth Estimation
This addresses the need for accurate bandwidth estimation in real-time video applications like Microsoft Teams, offering incremental improvements in user experience.
The paper tackles the problem of bandwidth estimation for real-time video applications by introducing Ivy, a method that uses offline meta-learning to dynamically select algorithms, enhancing user Quality of Experience by 5.9% to 11.2% over individual algorithms and 6.3% to 11.4% over online meta heuristics.
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.