In this work, we tackle the problem of bandwidth estimation (BWE) for real-time communication systems; however, in contrast to previous works, we leverage the vast efforts of prior heuristic-based BWE methods and synergize these approaches with deep learning-based techniques. Our work addresses challenges in generalizing to unseen network dynamics and extracting rich representations from prior experience, two key challenges in integrating data-driven bandwidth estimators into real-time systems. To that end, we propose Merlin, the first purely offline, data-driven solution to BWE that harnesses prior heuristic-based methods to extract an expert BWE policy. Through a series of experiments, we demonstrate that Merlin surpasses state-of-the-art heuristic-based and deep learning-based bandwidth estimators in terms of objective quality of experience metrics while generalizing beyond the offline world to in-the-wild network deployments where Merlin achieves a 42.85% and 12.8% reduction in packet loss and delay, respectively, when compared against WebRTC in inter-continental videoconferencing calls. We hope that Merlin's offline-oriented design fosters new strategies for real-time network control.

翻译：在本工作中，我们解决了实时通信系统中的带宽估计（BWE）问题；然而，与先前工作不同的是，我们利用了前人基于启发式的BWE方法的巨大努力，并将这些方法与基于深度学习的技术相结合。我们的工作解决了泛化到未见过的网络动态变化以及在先验经验中提取丰富表示这两大挑战，这是将数据驱动的带宽估计器集成到实时系统中的关键难题。为此，我们提出了Merlin，这是首个纯粹离线的、数据驱动的BWE解决方案，它利用先前基于启发式的方法来提取专家BWE策略。通过一系列实验，我们证明Merlin在客观体验质量指标上超越了最先进的基于启发式和基于深度学习的带宽估计器，同时能够泛化到离线世界之外的真实网络部署中。在洲际视频通话中，与WebRTC相比，Merlin分别将丢包率和延迟降低了42.85%和12.8%。我们希望Merlin以离线为导向的设计能催生实时网络控制的新策略。