Beyond QoS: Integrating Brain-Aware Constraints into Delay Bounds for uVR and Machine-Type Communication

Next-generation computing platforms are increasingly expected to accommodate a wide range of immersive applications, integrating advanced technologies such as extended reality and ultra-realistic virtual reality (uVR). The design and optimization of these distributed systems present significant challenges, particularly when managing the coexistence of traffic flows generated by both human and machine sources. This work introduces an end-to-end delay analysis framework focused on human-driven traffic in environments characterized by mixedsource flows. The human-generated streams—central to the analysis—coexist with machine-type communications that differ in terms of latency requirements. The delay analysis is carried out by deriving per-flow stochastic bounds, expressed in terms of the probability of receiving timely service. This is achieved through the use of stochastic network calculus and martingalebased methods, with human perceptual constraints explicitly incorporated into the theoretical model. Simulation-based validation confirms that the proposed bound accurately reflects actual end-to-end behavior. Moreover, the results demonstrate that integrating human perception into the analysis leads to improved performance compared to conventional approaches that do not account for cognitive aspects.