Toward Scalable VR-Cloud Gaming: An Attention-aware Adaptive Resource Allocation Framework for 6G Networks

TL;DR

This paper tackles the challenge of scalable VR cloud gaming over 6G by introducing a QoE-aware, multi-stage resource allocation framework that decomposes a complex joint optimization into three tractable subproblems: user association/PRB allocation, VR-CG engine placement with adaptive routing, and attention-driven end-to-end scheduling. It proposes a novel attention-based QoE model and semantic transmission approach, supported by three specialized heuristics (VEXA, GEPAR, AMPS) that deliver near-optimal performance with real-time feasibility. Datasets from VR hardware and games, along with a CNC-enabled 6G testbed, show substantial gains: up to 50% QoE improvement, 75% communication resource reduction, and 35% cost savings, with average optimality gaps around 5%. The work demonstrates scalable, real-time capable VR-CG provisioning in dense 6G networks and paves the way for practical deployment with open datasets and reproducible experiments.

Abstract

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.

Figures (13)

• Figure 1: VR-CG application architecture.
• Figure 2: NG-RAN topology.
• Figure 3: Attention-aware mechanism.
• Figure 4: VR-CG object-aware transmission model.
• Figure 5: Total and average QoE comparison.