CIVIC: Cooperative Immersion Via Intelligent Credit-sharing in DRL-Powered Metaverse
This addresses resource allocation problems for Metaverse service providers to enhance user immersion, though it appears incremental as it builds on existing DRL methods in a specific domain.
The paper tackled resource allocation challenges in the Metaverse by introducing the CIVIC framework, which uses Deep Reinforcement Learning to optimize cooperative sharing among service providers, achieving improvements such as 12-36% higher request completion and 23-70% higher fulfillment rates.
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.