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.

翻译：实时视频应用需要根据网络容量动态调整比特率，这要求精确的带宽估计。我们提出了一种新颖的带宽估计方法Ivy，该方法利用离线元学习来应对数据漂移并最大化用户体验质量。我们的方法能根据当前网络条件动态选择最合适的带宽估计算法，从而无需实时网络交互即可有效适应不断变化的环境。我们在Microsoft Teams中实现了该方法，并证明Ivy相较于单一带宽估计算法可将用户体验质量提升5.9%至11.2%，相较于现有在线元启发式方法可提升6.3%至11.4%。此外，我们的方法比在线元学习方法具有更高的数据效率，在显著减少训练数据需求的同时，可将用户体验质量提升高达21%。