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，这就要求对带宽进行准确估计（BWE）。我们引入了Ivy，一种新颖的BWE方法，它利用离线元学习来应对数据漂移并最大化用户质量体验（QoE）。我们的方法能够针对当前网络条件动态选择最合适的BWE算法，从而在无需实时网络交互的情况下有效适应变化的环境。我们在Microsoft Teams中实现了该方法，并证明Ivy相比单一BWE算法可提升QoE 5.9%至11.2%，相比现有在线元启发式方法可提升6.3%至11.4%。此外，我们表明与在线元学习方法相比，该方法具有更高的数据效率，在训练数据需求显著降低的情况下实现了高达21%的QoE改进。