Model Between Network Quality and XR User’s Experience in Terms of Video Stalling for Cloud Rendering XR

At present, extended reality (XR) is a hotspot of research around the world. The cloud rendering XR based on mobile communication system provides a significant solution to reducing the demand of head-mounted display (HMD) for computing power, thus the scope of application for XR can be expanded from indoor to outdoor. However, cloud rendering XR has more demanding requirements on network quality, failure to meet these requirements leads to poor user experience. In particular, video streaming stalling is the most critical factor affecting the immersive experience, and it is closely related to network quality. Therefore, it is essential that the network is capable of predicting the user experience of stalling for precise resource scheduling and allocation. In order to reveal the relationship between network quality and stalling, a frame-level data transmission framework with two-layer mapping model is proposed in this paper. The framework uses indicators like packet loss, latency, throughput, and forward error correction (FEC) to model video frame loss in the lower layer. The upper layer then uses this information to predict stalling probability. Furthermore, simulation and experiment are implemented to evaluate the accuracy and feasibility of the proposed model in the 5th generation mobile communication technology (5G). The experimental results indicate that the proposed model performs extremely well in addressing and modeling the mapping relationship between network quality and video stalling for cloud rendering XR.