The rapid growth of multi-user eXtended Reality (XR) applications, spanning fields such as entertainment, education, and telemedicine, demands seamless, immersive experiences for users interacting within shared, distributed environments. Delivering such latency-sensitive experiences involves considerable challenges in orchestrating network, computing, and service resources, where existing limitations highlight the need for a structured approach to analyse and optimise these complex systems. This challenge is amplified by the need for high-performance, low-latency connectivity, where 5G and 6G networks provide essential infrastructure to meet the requirements of XR services at scale. This article addresses these challenges by developing a model that parametrises multi-user XR services across four critical layers of the standard virtualisation architecture. We formalise this model mathematically, proposing a context-aware framework that defines key parameters at each level and integrates them into a comprehensive Edge-Cloud Continuum orchestration strategy. Our contributions include a detailed analysis of the current limitations and needs in existing Edge-Cloud Continuum orchestration approaches, the formulation of a layered mathematical model, and a validation framework that demonstrates the utility and feasibility of the proposed solution.

翻译：随着多用户扩展现实（XR）应用在娱乐、教育和远程医疗等领域的快速增长，用户对在共享分布式环境中交互时获得无缝沉浸式体验的需求日益迫切。提供此类对延迟敏感的服务体验，需要在网络、计算和服务资源的编排方面应对重大挑战，而现有方法的局限性凸显了采用结构化方法来分析和优化这些复杂系统的必要性。高性能、低延迟连接的需求进一步加剧了这一挑战，其中5G和6G网络为大规模满足XR服务需求提供了关键基础设施。本文通过开发一个在标准虚拟化架构四个关键层级上对多用户XR服务进行参数化的模型来应对这些挑战。我们以数学形式严格表述该模型，提出一种上下文感知框架，该框架定义了各层级的关键参数，并将其整合到全面的边缘-云连续体编排策略中。我们的贡献包括：对现有边缘-云连续体编排方法的局限性和需求进行详细分析，构建分层数学模型，以及建立验证框架以证明所提方案的实用性和可行性。