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.

翻译：沉浸式虚拟现实（VR）应用对延迟、能效和计算资源提出了严格要求，尤其是在多用户交互场景中。为应对这些挑战，我们引入了空间计算通信（SCC）的概念，这是一个旨在满足分布式移动边缘计算（MEC）网络上多用户VR的延迟与能耗需求的框架。SCC通过用户动态与资源需求的概率模型，联合表征由用户和基站定义的物理空间，以及代表共享沉浸式环境的虚拟空间。随后，资源部署任务被构建为一个多目标组合优化（MOCO）问题，旨在同时最小化分布式MEC资源上的系统延迟与能耗。为解决此问题，我们提出了MO-CMPO，一种结合策略优化的多目标一致性模型，它整合了由偏好权重指导的监督学习与强化学习（RL）微调。MO-CMPO利用稀疏图神经网络（GNN）高效生成帕累托最优解。基于真实世界新无线电基站数据集的仿真表明，相较于基线方法，MO-CMPO实现了更优的超体积性能并显著降低了推理延迟。此外，分析揭示了实际的部署模式：面向延迟的解决方案倾向于本地MEC执行以减少传输延迟，而面向能耗的解决方案则通过最小化冗余部署来节省能量。