Online Multi-User Scheduling for XR Transmissions With Hard-Latency Constraint: Performance Analysis and Practical Design
Xiaoyu Zhao, Ying–Jun Angela Zhang, Meng Wang, Xiang Chen, Yue Li
Abstract
Extended reality (XR) is an emerging 6G application with unique traffic characteristics and requirements, calling for innovative Ultra-Reliable and Low-Latency Communication (URLLC) technologies. This paper investigates multi-user scheduling to meet XR services’ hard-latency constraints. Specifically, we focus on a periodical traffic model, where the latency constraint for transmitting each XR frame is less than the inter-arrival time. We describe the system as a periodic Markov Decision Process (MDP) with the performance metric being the probability of successful transmission within the latency constraint. We then obtain the maximum success probability and the optimal scheduling based on the optimal value function. In the case of homogeneous arrivals, we construct a lower bound of the optimal value function. Based on this, we propose an online multi-user scheduling policy that determines scheduling decisions by solving a series of nonlinear Knapsack Problems (KPs) in polynomial time. Our analysis demonstrates that the scheduling scheme is asymptotically optimal with increasing users. Furthermore, we extend the online scheduling scheme to heterogeneous arrivals and present extensions for practical scenarios with multiple resource blocks, quasi-periodical arrivals, random frame sizes, and time-correlated channel fading. Finally, simulation results show that the proposed scheduler achieves near-optimal performance and outperforms other benchmark schedulers.