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

• URL: http://arxiv.org/abs/2510.14243v1
• Date: Thu, 16 Oct 2025 02:55:01 GMT
• Title: Spatial Computing Communications for Multi-User Virtual Reality in Distributed Mobile Edge Computing Network
• Authors: Caolu Xu, Zhiyong Chen, Meixia Tao, Li Song, Wenjun Zhang,
• Abstract summary: Immersive virtual reality (VR) applications impose stringent requirements on latency, energy efficiency, and computational resources.<n>We introduce the concept of computing communications ( SCC), a framework designed to meet the latency and energy demands of multi-user VR over distributed mobile edge computing (MEC) networks.<n>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.
• Score: 45.55309713021969
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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