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.

我们提出了Merlin，这是一种用于带宽估计的纯离线数据驱动解决方案，通过将启发式方法与深度学习技术相结合，克服了泛化到未见网络动态和从先前经验中提取丰富表示的挑战，在实时系统中集成数据驱动的带宽估计器。通过一系列实验，我们证明了Merlin在客观体验质量指标方面超过了最先进的启发式和深度学习带宽估计器，在野外网络部署中，与WebRTC在洲际视频会议呼叫中相比，Merlin在丢包率和延迟方面分别实现了42.85%和12.8%的降低。我们希望Merlin的离线设计能促进实时网络控制的新策略。

离线专家演示的实时带宽估计