面向质量经验优化的实时视频通信的人类在回路带宽估计

视频会议系统所提供的质量经验（QoE）显著受到准确估计sender和receiver之间可用时间变化带宽的影响。实时通信的带宽估计仍是一个开放性挑战，due to rapidly evolving network architectures, increasingly complex protocol stacks, and the difficulty of defining QoE metrics that reliably improve user experience。 In this work, we propose a deployed, human-in-the-loop, data-driven framework for bandwidth estimation to address these challenges。 Our approach begins with training objective QoE reward models derived from subjective user evaluations to measure audio and video quality in real-time video conferencing systems。 Subsequently, we collect roughly $1$M network traces with objective QoE rewards from real-world Microsoft Teams calls to curate a bandwidth estimation training dataset。 We then introduce a novel distributional offline reinforcement learning (RL) algorithm to train a neural-network-based bandwidth estimator aimed at improving QoE for users。 Our real-world A/B test demonstrates that the proposed approach reduces the subjective poor call ratio by $11.41\%$ compared to the baseline bandwidth estimator。 Furthermore, the proposed offline RL algorithm is benchmarked on D4RL tasks to demonstrate its generalization beyond bandwidth estimation。