User experience in real-time video applications requires continuously adjusting video encoding bitrates to match available network capacity, which hinges on accurate bandwidth estimation (BWE). However, network heterogeneity prevents a one-size-fits-all solution to BWE, motivating the demand for personalized approaches. Although personalizing BWE algorithms offers benefits such as improved adaptability to individual network conditions, it faces the challenge of data drift -- where estimators degrade over time due to evolving network environments. To address this, we introduce Ivy, a novel method for BWE that leverages offline metalearning to tackle data drift and maximize end-user Quality of Experience (QoE). Our key insight is that dynamically selecting the most suitable BWE algorithm for current network conditions allows for more effective adaption to changing environments. Ivy is trained entirely offline using Implicit Q-learning, enabling it to learn from individual network conditions without a single, live videoconferencing interaction, thereby reducing deployment complexity and making Ivy more practical for real-world personalization. We implemented our method in a popular videoconferencing application 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.

翻译：实时视频应用中的用户体验需要持续调整视频编码比特率以匹配可用网络容量，这依赖于精确的带宽估计（BWE）。然而，网络异构性使得BWE无法采用一刀切的解决方案，从而催生了对个性化方法的需求。尽管个性化BWE算法具有诸多优势，例如能更好地适应个体网络条件，但其面临数据漂移的挑战——即由于网络环境动态演变，估计器的性能会随时间退化。为解决这一问题，我们提出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终端用户的体验质量（QoE）。我们的核心洞见在于：动态选择最适合当前网络条件的BWE算法，能够更有效地适应不断变化的环境。Ivy完全采用隐式Q学习进行离线训练，使其能够从个体网络条件中学习，而无需任何实时视频会议交互，从而降低了部署复杂度，使Ivy在实际个性化应用中更具可行性。我们在主流视频会议应用中实现了该方法，并证明Ivy相较于单一BWE算法可将QoE提升5.9%至11.2%，相较于现有在线元启发式方法则可提升6.3%至11.4%。