Social VR aims at enabling people located at different places to communicate and interact with each other in a natural way. It poses extremely strong throughput and latency requirements on the underlying communication networks. This paper investigates the potential of using cross-layer design approaches for radio access scheduling in order to realize these challenging requirements in (beyond) 5G networks. In particular, we provide an in-depth simulation study of the performance/capacity gains that can be achieved by exploiting the end-to-end latency budget and/or video frame type as cross-layer information in the scheduling decisions, and show how the benefits depend on the actual social VR scenario. This study further reveals the importance of using application-level metrics such as PSNR or SSIM rather than traditional network-level metrics like the packet drop rate in the performance assessment.@en

This study proposes a two-step ML-based multislice radio resource allocation framework for 5G networks, specifically for emergency scenarios and featuring a good tradeoff between complexity and performance. In the first step, call-level resource demands are predicted using supervised ML, which are then aggregated to predict slice-specific resource demands. An innovative method is included in this step to ensure the collection of representative training data for the supervised ML. In the second step, a contextual multi-armed bandit reinforcement learning model is applied to derive the resource allocation among the slices based on the slice-specific resource demand predictions. The simulation results show that the proposed framework outperforms alternative solutions in the defined utility values for priority emergency traffic at the cost of modest performance sacrifice of the background traffic.

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