Cross-layer Optimization of MAC Scheduling for Multi-User Virtual Reality over 5G

Abstract

As a result of a global pandemic, there has been an increasing interest in tools for remote video conferencing and collaboration. One of these new innovations is social eXtended Reality (XR). By combining Virtual Reality (VR) and Augmented Reality (AR) technologies, social XR can provide a more immersive experience than any other VR application by giving users at different locations the chance to virtually gather in real-time. But such applications impose enormous requirements on computational and communication resources. 5th Generation (5G) mobile networks are targeted as solution to provide ultra-low latency and ultra-high throughput for social XR. In current research, many optimisations are aimed at VR applications such as on-demand streaming, while there is a lack of solutions for real-time user-interactive applications like social XR. In this graduation project we develop and assess cross-layer solutions for optimised scheduling of social XR applications in 5G networks. An existing framework for simulating social XR conference applications serves as the basis for our modelling approach. We devise different schedulers, that utilise cross-layer information in the form of the video frametype and frame-level End-to-End (E2E) latency budgets rather than packet-level latency budgets purely within the Radio Access Network (RAN). In contrast to previous work, we create the VR traffic based on real video data and develop tools to model the packet dispersion caused by multi-hop transmission over the internet towards the RAN. We study the effect of various system and traffic parameters on the Quality of Service (QoS) and perceived Quality of Experience (QoE) in the context of social XR applications through an extensive sensitivity analysis. Herein we also assess the performance impact of different types of cross-layer packet schedulers. Further, we gain insights into the correlation between the network QoS and perceived QoE by end users which are the key in future cross-layer implementations for social XR.