ACM MMSys 2024 实时通信挑战中的带宽估计
ACM MMSys 2024 Bandwidth Estimation in Real Time Communications Challenge
AI总结：
该挑战利用离线强化学习和真实世界数据，将奖励与用户感知QoE对齐，以改进RTC带宽估计模型，获胜模型性能与顶级行为策略相当。
摘要：
视频会议系统向最终用户提供的体验质量（QoE）部分取决于随时间准确估计发送端与接收端之间瓶颈链路的容量。实时通信（RTC）的带宽估计仍然是一个重大挑战，这主要归因于不断演进的异构网络架构和技术。从首届在ACM MMSys 2021上举办的带宽估计挑战赛中，我们了解到，在模拟环境中使用强化学习（RL）训练并最大化基于网络的奖励函数的带宽估计模型，可能在实际中并非最优，原因在于模拟到现实的差距以及难以将基于网络的奖励与用户感知的QoE对齐。本次大挑战旨在通过使用离线强化学习和一个带有客观奖励的真实世界数据集（该数据集中的客观奖励与Microsoft Teams中的主观音视频质量具有高度相关性），将奖励最大化与用户感知的QoE优化对齐，从而推进带宽估计模型的设计。所有提交给大挑战的模型都在我们的仿真平台上进行了初步评估。为了在具有时间波动的多样化网络条件下进行全面评估，排名靠前的模型进一步在我们的地理分布式测试平台上进行了评估，每个模型在12天内进行了600次通话。获胜模型被证明在发布的数据集中表现出与顶级行为策略相当的性能。通过利用真实世界数据并将客观音视频质量评分作为奖励整合进来，离线强化学习因此能够促进RTC中具有竞争力的带宽估计器的发展。
英文摘要：
The quality of experience (QoE) delivered by video conferencing systems to end users depends in part on correctly estimating the capacity of the bottleneck link between the sender and the receiver over time. Bandwidth estimation for real-time communications (RTC) remains a significant challenge, primarily due to the continuously evolving heterogeneous network architectures and technologies. From the first bandwidth estimation challenge which was hosted at ACM MMSys 2021, we learned that bandwidth estimation models trained with reinforcement learning (RL) in simulations to maximize network-based reward functions may not be optimal in reality due to the sim-to-real gap and the difficulty of aligning network-based rewards with user-perceived QoE. This grand challenge aims to advance bandwidth estimation model design by aligning reward maximization with user-perceived QoE optimization using offline RL and a real-world dataset with objective rewards which have high correlations with subjective audio/video quality in Microsoft Teams. All models submitted to the grand challenge underwent initial evaluation on our emulation platform. For a comprehensive evaluation under diverse network conditions with temporal fluctuations, top models were further evaluated on our geographically distributed testbed by using each model to conduct 600 calls within a 12-day period. The winning model is shown to deliver comparable performance to the top behavior policy in the released dataset. By leveraging real-world data and integrating objective audio/video quality scores as rewards, offline RL can therefore facilitate the development of competitive bandwidth estimators for RTC.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
