Wireless Multi-User Interactive Virtual Reality in Metaverse with Edge-Device Collaborative Computing
<|reference_start|>Wireless Multi-User Interactive Virtual Reality in Metaverse with Edge-Device Collaborative Computing: The immersive nature of the metaverse presents significant challenges for wireless multi-user interactive virtual reality (VR), such as ultra-low latency, high throughput and intensive computing, which place substantial demands on the wireless bandwidth and rendering resources of mobile edge computing (MEC). In this paper, we propose a wireless multi-user interactive VR with edge-device collaborative computing framework to overcome the motion-to-photon (MTP) threshold bottleneck. Specifically, we model the serial-parallel task execution in queues within a foreground and background separation architecture. The rendering indices of background tiles within the prediction window are determined, and both the foreground and selected background tiles are loaded into respective processing queues based on the rendering locations. To minimize the age of sensor information and the power consumption of mobile devices, we optimize rendering decisions and MEC resource allocation subject to the MTP constraint. To address this optimization problem, we design a safe reinforcement learning (RL) algorithm, active queue management-constrained updated projection (AQM-CUP). AQM-CUP constructs an environment suitable for queues, incorporating expired tiles actively discarded in processing buffers into its state and reward system. Experimental results demonstrate that the proposed framework significantly enhances user immersion while reducing device power consumption, and the superiority of the proposed AQM-CUP algorithm over conventional methods in terms of the training convergence and performance metrics.<|reference_end|>
@article{xu2024wireless,
    title={Wireless Multi-User Interactive Virtual Reality in Metaverse with
  Edge-Device Collaborative Computing},
    author={Caolu Xu, Zhiyong Chen, Meixia Tao, and Wenjun Zhang},
    journal={arXiv preprint arXiv:2407.20523},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.20523},
    primaryClass={cs.IT cs.MM math.IT}
}