QoE-Oriented Cooperative VR Rendering and Dynamic Resource Leasing in Metaverse

Nan Liu, Tom H. Luan, Yuntao Wang, Yiliang Liu, Zhou Su

Abstract: The rise of the Metaverse has ushered in a new era of social networking, offering users deeply engaging spaces to connect and participate in social activities. However, rendering these virtual environments is resource-intensive. With many users accessing simultaneously and requiring diverse Metaverse services, optimizing Metaverse resources to deliver the best quality-of-experience (QoE) for users is a significant challenge. In this paper, we propose a cooperative virtual reality (VR) rendering and dynamic resource leasing mechanism to address this issue. Specifically, we first introduce a cooperative VR scene pre-rendering framework between users and Planets (i.e., edge servers hosting users), and establish a new user QoE metric named EdgeVRQoE which considers both rendering delay and visual quality. We formulate the multidimensional rendering resources (e.g., GPU, CPU, and outbound bandwidth) leasing problem between Planets and users as a double-layer decision problem, and devise a hybrid action multi-agent reinforcement learning-based dynamic resource auction mechanism to efficiently allocate limited resources of Planets in a distributed and adaptive manner. Extensive simulations demonstrate that our proposed scheme outperforms the representatives in user QoE and resource utilization efficiency. Particularly, the proposed scheme shows at least an 18-fold improvement in QoE over other schemes, demonstrating its capability in providing immersive Metaverse experiences.

External IDs:dblp:journals/tmc/LiuLWLS25