CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse

2026-04-02

AI Summary

This study addresses the challenge of resource allocation in the metaverse, where diverse virtual environments, complex digital twin synchronization, dynamic user demands, and stringent immersion requirements complicate efficient provisioning. To tackle this, the authors propose a multi-provider collaborative framework that introduces, for the first time, an intelligent credit-sharing mechanism, integrating non-cooperative strategies with cooperative resource pooling based on a universal credit system to enable immersion-aware dynamic scheduling and fair allocation. By modeling and solving two NP-hard resource allocation problems via deep reinforcement learning, the approach achieves significant improvements: request completion rates increase by 12–36%, satisfaction rates rise by 23–70%, the number of served users grows by 20–60%, and allocation fairness improves by up to 51%, all while maintaining cost competitiveness.

Abstract

The Metaverse faces complex resource allocation challenges due to diverse Virtual Environments (VEs), Digital Twins (DTs), dynamic user demands, and strict immersion needs. This paper introduces CIVIC (Cooperative Immersion Via Intelligent Credit-sharing), a novel framework optimizing resource sharing among multiple Metaverse Service Providers (MSPs) to enhance user immersion. Unlike existing methods, CIVIC integrates VE rendering, DT synchronization, credit sharing, and immersion-aware provisioning within a cooperative multi-MSP model. The resource allocation problem is formulated as two NP-hard challenges: a non-cooperative setting where MSPs operate independently and a cooperative setting utilizing a General Credit Pool (GCP) for dynamic resource sharing. Using Deep Reinforcement Learning (DRL) for tuning resources and managing cooperating MSPs, CIVIC achieves 12-36% higher request completion, 23-70% higher fulfillment rates, 20-60% more served clients, and up to 51% more fairly distributed requests, all with competitive costs. Extensive experiments demonstrate CIVIC's resilience, adaptability, and robust performance under dynamic load conditions and unexpected demand surges, making it suitable for real-world distributed Metaverse infrastructures.

Problem

• Metaverse
• resource allocation
• user immersion
• Virtual Environments
• Digital Twins

Innovation

• Credit-sharing
• Deep Reinforcement Learning
• Metaverse Resource Allocation
• Cooperative Multi-MSP
• Immersion-aware Provisioning