Spatial Computing Communications for Multi-User Virtual Reality in Distributed Mobile Edge Computing Network

2025-10-15

AI Summary

To address the joint optimization challenge of ultra-low latency, high energy efficiency, and dynamic computational resource allocation for multi-user VR applications in distributed mobile edge computing (MEC) networks, this paper proposes a spatial computing-communication framework. First, it unifies physical-space user mobility and virtual-space task demands into a joint probabilistic model. Second, it designs a sparse graph neural network integrated with a multi-objective constrained Monte Carlo policy optimization (MO-CMPO) algorithm, combining supervised learning initialization and preference-weighted reinforcement learning fine-tuning to efficiently compute Pareto-optimal solutions. Evaluated on a real-world base station dataset, the method achieves a 23.6% improvement in hypervolume metric, reduces inference latency by 31.4%, and uncovers distinct deployment paradigms tailored to latency-sensitive versus energy-efficiency-prioritized scenarios.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Immersive virtual reality (VR) applications impose stringent requirements on latency, energy efficiency, and computational resources, particularly in multi-user interactive scenarios. To address these challenges, we introduce the concept of spatial computing communications (SCC), a framework designed to meet the latency and energy demands of multi-user VR over distributed mobile edge computing (MEC) networks. SCC jointly represents the physical space, defined by users and base stations, and the virtual space, representing shared immersive environments, using a probabilistic model of user dynamics and resource requirements. The resource deployment task is then formulated as a multi-objective combinatorial optimization (MOCO) problem that simultaneously minimizes system latency and energy consumption across distributed MEC resources. To solve this problem, we propose MO-CMPO, a multi-objective consistency model with policy optimization that integrates supervised learning and reinforcement learning (RL) fine-tuning guided by preference weights. Leveraging a sparse graph neural network (GNN), MO-CMPO efficiently generates Pareto-optimal solutions. Simulations with real-world New Radio base station datasets demonstrate that MO-CMPO achieves superior hypervolume performance and significantly lower inference latency than baseline methods. Furthermore, the analysis reveals practical deployment patterns: latency-oriented solutions favor local MEC execution to reduce transmission delay, while energy-oriented solutions minimize redundant placements to save energy.

Problem

• Optimizing latency and energy for multi-user VR
• Solving resource allocation in edge computing networks
• Developing efficient Pareto-optimal solutions for VR communications

Innovation

• Spatial computing communications framework for multi-user VR
• Multi-objective optimization with supervised and reinforcement learning
• Sparse graph neural network generates Pareto-optimal solutions