Real-Time Super-Resolution: A New Mechanism for XR over 5G-Advanced

Weichao Chen, Youlong Cao, Yi Qin, Erkai Chen, Guohua Zhou, Weichao Li

Abstract: Extended Reality (XR) has attracted great attention from both academic and industry, for providing users with an immersive experience anywhere. Nowadays, XR video streaming service is evolving to high definition (HD), which results in massive data traffic with more stringent latency requirement. Due to the two characteristics, it is challenging to support the commercial use for XR service in the current New Ratio (NR) network. In this paper, we propose a real-time super-resolution (RTSR) framework for XR HD video transmission. The basic idea is to utilize the overfitting feature of Deep Neural Network (DNN) to learn the non-linear mapping between low-definition (LD) video frames and HD video frames. The cloud XR server can transmit the LD frames together with the dedicated super resolution (SR) models instead of sending HD frames directly. The receiver can recover the HD frames locally with the inferencing ability of SR model. In addition, by introducing the online training and layered transmission strategy, the SR model update period can be adaptively adjusted according to the scenario changes, which also reduces the transmission overhead. Simulation results demonstrate the superiority of our proposed RTSR, which can save up to 50% traffic and increase the XR capacity about 40% compared with the conventional SR scheme. In terms of the system capacity, our results show that the average number of UEs can reach about 23 per cell under the common settings of Dense Urban.