Adaptive RSU Assignment and Transmission Scheduling of Delay-Critical Emergency Messages and AR Traffic in MEC-Enabled Vehicular Environments

Emergency messages and augmented reality (AR) are becoming integral to intelligent vehicular systems, but their existence poses significant challenges due to conflicting requirements. Emergency short messages demand ultra-low latency and strict reliability, while AR contents require larger data transfers with more flexible but still location-sensitive deadlines. To address this, a joint problem of roadside unit (RSU) assignment and transmission scheduling in multi-server, multi-user MEC-enabled vehicular networks is studied. The problem is formulated as an NP-hard optimization task and a two-stage framework is proposed. First, the penalty-minimizing RSU selection (PMRS) algorithm assigns requested content to RSUs by minimizing combined deadline and coverage penalties. Then a hybrid scheduling algorithm called deadline-aware priority scheduling (DAPS) is proposed, which integrates earliest-deadline-first and simulated annealing to prioritize emergency traffic while efficiently serving AR content. We benchmark the proposed framework against classical heuristics and metaheuristics. The results verify that the proposed approach can outperform the baseline methods under various realistic vehicular mobility and traffic conditions.