Virtual Reality Cloud Gaming (VR-CG) represents a demanding class of immersive applications, requiring high bandwidth, ultra-low latency, and intelligent resource management to ensure optimal user experience. In this paper, we propose a scalable and QoE-aware multi-stage optimization framework for resource allocation in VR-CG over 6G networks. Our solution decomposes the joint resource allocation problem into three interdependent stages: (i) user association and communication resource allocation; (ii) VR-CG game engine placement with adaptive multipath routing; and (iii) attention-aware scheduling and wireless resource allocation based on motion-to-photon latency. For each stage, we design specialized heuristic algorithms that achieve near-optimal performance while significantly reducing computational time. We introduce a novel user-centric QoE model based on visual attention to virtual objects, guiding adaptive resolution and frame rate selection. A dataset-driven evaluation demonstrates that, when compared against state-of-the-art approaches, our framework improves QoE by up to 50\%, reduces communication resource usage by 75\%, and achieves up to 35\% cost savings, while maintaining an average optimality gap of 5\%. Our proposed heuristics solve large-scale scenarios in under 0.1 seconds, highlighting their potential for real-time deployment in next-generation mobile networks.

翻译：虚拟现实云游戏（VR-CG）代表了一类对资源要求极高的沉浸式应用，需要高带宽、超低延迟和智能资源管理以确保最佳用户体验。本文提出了一种面向6G网络中VR-CG资源分配的可扩展且感知服务质量的多阶段优化框架。该方案将联合资源分配问题分解为三个相互依赖的阶段：（i）用户关联与通信资源分配；（ii）基于自适应多路径路由的VR-CG游戏引擎部署；（iii）基于运动到光子延迟的注意力感知调度与无线资源分配。针对每个阶段，我们设计了专门的启发式算法，在显著降低计算时间的同时实现接近最优的性能。我们引入了一种基于用户对虚拟对象视觉注意力的新型以用户为中心的服务质量模型，用于指导自适应分辨率与帧率选择。基于数据集的评估表明，与现有先进方法相比，本框架可将服务质量提升最高50%，通信资源使用量降低75%，并实现最高35%的成本节约，同时保持平均5%的最优性差距。所提出的启发式算法可在0.1秒内解决大规模场景问题，凸显了其在下一代移动网络中实时部署的潜力。