Simulating extended reality traffic: An empirical model from user behavior to network packets

Several components in the design of next-generation networks, including user profiling and network slicing, rely on accurate models of traffic load. In this context, recent studies have focused on various video traffic categories, while traffic associated with extended reality (XR) services has received limited attention. This paper introduces a novel empirical model for 3D XR traffic, developed by encoding real Point Clouds using a standard-compliant codec, and able to account for the dynamic of service sessions and user behaviors over an entire session. Our methodology encompasses multiple temporal scales, ranging from milliseconds to minutes, to account for different phenomena related to both user behavior and encoder settings. Initially, we investigate the packet size distribution at the time scale of a semantic unit, corresponding to the encoding of a single point cloud. We verify that it can be effectively represented by a heavy-tailed Gamma distribution. Then, we illustrate how this insight can be leveraged to model application-layer phenomena. Specifically, we demonstrate the applicability of a general semi-hidden Markov model to capture both the temporal dynamics of service sessions and user behaviors. We provide results in terms of comparison of the empirical and fitting traffic distributions, based on quantile to quantile analysis and statistical tests. We also show how the model can be trained on real data and we provide a pseudo-code demonstrating the model application within a network simulator.