Joint Communication and Computation Resource Allocation for Emerging mmWave Multi-User 3D Video Streaming Systems

We consider a multi-user joint rate adaptation and computation distribution problem in a millimeter wave (mmWave) virtual reality (VR) system. The VR system that we consider comprises an edge computing unit (ECU) that serves 360° videos to VR users. We formulate a multi-user quality of experience (QoE) maximization problem, in which VR users are assisted with the ECU to decode/render 360° videos. The ECU provides additional computational resources that can be used for processing video frames, at the expense of increased data volume and required bandwidth. To balance this trade-off, we leverage deep reinforcement learning (DRL) for joint rate adaptation and computational resource allocation optimization. Our proposed method, dubbed Deep VR, does not rely on any predefined assumption about the environment and relies on video playback statistics (i.e., past throughput, decoding time, transmission time, etc.), video information, and the resulting performance to adjust the video bitrate and computation distribution. We train Deep VR with real-world mmWave network traces and 360° video datasets to obtain evaluation results in terms of the average QoE, peak signal-to-noise ratio (PSNR), rebuffering time, and quality variation. Our results indicate that the Deep VR improves the users' QoE compared to state-of-the-art rate adaptation algorithm. Specifically, we show a 3.08 dB to 4.49 dB improvement in video quality in terms of PSNR, a 12.5x to 14x reduction in rebuffering time, and a 3.07 dB to 3.96 dB improvement in quality variation.