  We study a multi-task decision-making problem for 360 video processing in a
wireless multi-user virtual reality (VR) system that includes an edge computing
unit (ECU) to deliver 360 videos to VR users and offer computing assistance for
decoding/rendering of video frames. However, this comes at the expense of
increased data volume and required bandwidth. To balance this trade-off, we
formulate a constrained quality of experience (QoE) maximization problem in
which the rebuffering time and quality variation between video frames are
bounded by user and video requirements. To solve the formulated multi-user QoE
maximization, we leverage deep reinforcement learning (DRL) for multi-task rate
adaptation and computation distribution (MTRC). The proposed MTRC approach 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 MTRC with real-world wireless
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 MTRC improves the users' QoE
compared to state-of-the-art rate adaptation algorithm. Specifically, we show a
5.97 dB to 6.44 dB improvement in PSNR, a 1.66X to 4.23X improvement in
rebuffering time, and a 4.21 dB to 4.35 dB improvement in quality variation.
