Virtual Reality in Metaverse over Wireless Networks with User-centered Deep Reinforcement Learning

TL;DR

This paper proposes a user-centered deep reinforcement learning method for optimizing VR computation offloading in wireless networks within the Metaverse, enhancing immersive social experiences.

Contribution

It introduces a novel user-centered deep reinforcement learning approach for multi-user VR offloading over wireless networks, addressing the unique needs of human users.

Findings

Approach achieves near-optimal offloading performance.

Significant improvements in latency and resource utilization.

Robustness across various network conditions.

Abstract

The Metaverse and its promises are fast becoming reality as maturing technologies are empowering the different facets. One of the highlights of the Metaverse is that it offers the possibility for highly immersive and interactive socialization. Virtual reality (VR) technologies are the backbone for the virtual universe within the Metaverse as they enable a hyper-realistic and immersive experience, and especially so in the context of socialization. As the virtual world 3D scenes to be rendered are of high resolution and frame rate, these scenes will be offloaded to an edge server for computation. Besides, the metaverse is user-center by design, and human users are always the core. In this work, we introduce a multi-user VR computation offloading over wireless communication scenario. In addition, we devised a novel user-centered deep reinforcement learning approach to find a near-optimal solution. Extensive experiments demonstrate that our approach can lead to remarkable results under various requirements and constraints.