Virtual Reality Cloud Gaming (VR-CG) is a demanding class of immersive applications that require high bandwidth, ultra-low latency, and efficient resource allocation to deliver a high-quality user experience. In this paper, we propose a scalable, QoE-aware multi-stage optimization framework for VR-CG over 6G networks. Our approach decomposes the joint resource allocation problem into three 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 under motion-to-photon latency constraints. For each stage, we design specialized heuristic algorithms that achieve near-optimal performance with significantly reduced computational complexity. We further introduce a user-centric QoE model based on visual attention to virtual objects, enabling adaptive selection of resolution and frame rate. Extensive evaluations using real-world datasets show that, compared to state-of-the-art approaches, the proposed 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%. Moreover, the proposed heuristics solve large-scale scenarios in under 0.1 s, demonstrating their suitability for real-time deployment in next-generation mobile networks.

The future mobile network schedulers have the complex mission of distributing radio resources among various applications with different requirements. The radio access network (RAN) slicing enables the creation of different logical networks by using dedicated resources for each group of applications. In this scenario, the radio resource scheduling (RRS) is responsible for distributing the radio resources among the slices to fulfill their requirements. Several recent studies have proposed advances in machine learning-based RRS. However, these works often evaluate their models under limited scenarios and with minimal slice diversity, raising concerns about their real-world applicability. The generalization capabilities of these models remain uncertain without rigorous testing across diverse network conditions and slice configurations, which may hinder their effectiveness upon deployment in operational networks. This paper proposes an intent-based RRS using multi-agent reinforcement learning in a RAN slicing context. The proposed method protects high-priority slices when the available radio resources are insufficient, using transfer learning to reduce the number of required training steps. The proposed method and baselines are evaluated in different network scenarios that comprehend combinations of different slice types, channel trajectories, number of active slices and users' equipment (UEs), and UE characteristics. The proposed method outperformed the baselines in protecting slices with higher priority, obtaining an improvement of 40% and, when considering all the slices, obtaining an improvement of 20% in relation to the baselines.

Network slicing at the radio access network (RAN) domain, called RAN slicing, requires elasticity, efficient resource sharing, and customization. In this scenario, radio resource scheduling (RRS) is responsible for dealing with scarce and limited frequency spectrum resources available at the RAN domain while fulfilling the slice intents. The wide variety of scenarios supported in 5G and beyond 5G networks makes the RRS problem in RAN slicing scenario a significant challenge. This paper proposes an intent-aware reinforcement learning method to perform the RRS function in a RAN slicing scenario. The slice’s quality of service intents is described in a common intent model in a service-level agreement. The proposed method tries to prevent intent faults by making the management of radio resources available among slices. This method uses slices’ and user equipment network metrics in the observation space. The proposed method is evaluated under different network conditions and outperforms different baselines considering the slices’ intents fulfillment.