Online Multi-User Scheduling for Extended Reality Transmissions with Hard-Latency Constraint

In the forthcoming 6G era, Extended reality (XR) is an emerging application with unique traffic characteristics requirements, calling for innovative Ultra-Reliable and Low-Latency Communication (URLLC) technologies. In this paper, we investigate multi-user scheduling to meet hard-latency constraints for XR services. Specifically, we focus on a periodical XR traffic model, where the latency constraint for transmitting each XR frame is less than the inter-arrival time. To find an optimal multi-user scheduling scheme, we first describe the system as a periodic Markov Decision Process (MDP), where the scheduling performance is expressed as the probability of successful transmission within the latency constraint. Then, we obtain the maximum success probability and the optimal scheduling based on the optimal value function. Inspired by the properties of the optimal value function, we construct a lower bound of it and propose an online multi-user scheduling scheme. In particular, scheduling decisions under the proposed scheme are determined by solving a series of nonlinear Knapsack Problem (KP) in polynomial time. Finally, simulation results show that the proposed scheduler achieves nearly optimal performance and outperforms other benchmark schedulers.