Enhancing Uplink Scheduling in 5G Enabled Vehicular Networks: A Cross-Layer Approach with Predictive Buffer Status Reporting

Enabling widespread adoption of resource-intensive vehicular applications such as Extended Reality (XR) and High Definition map (HD Map) necessitates further enhancements in 5G, which is anticipated with 5G-Advanced. These applications, sensitive to latency, prompt researchers to propose offloading vehicles' complex computations to nearby edge clouds, aiming to minimize latency and meeting the Quality-of-Service (QoS) demands of these applications. However, the uncertainties arising from spatio-temporal factors due to vehicle mobility and the dynamic nature of application behaviour pose significant challenges in deciding the efficient offloading decision for minimizing latency. To tackle this challenge, this paper introduces a crosslayer framework that bridges the Radio Access Network (RAN) scheduler with the Mobile Edge Computing (MEC) scheduler. The proposed framework facilitates the exchange of vehicle ranks and channel condition information between schedulers, strategically aimed at reducing Head-Of-Line (HOL) delay for efficient computational offloading. Furthermore, the MAC layer incorporates the prediction of the Buffer Status Report (BSR) using Machine Learning (ML) to further reduce the queuing delay experienced by the offloading jobs of the vehicles in uplink. Simulation results using the NS-3 gym demonstrate that the proposed cross-layer framework achieves a higher Offloading Success Rate (OSR) than the state-of-the-art QoS scheduler by effectively reducing HOL delay for HD Map vehicular application.