The growth of multimedia applications such as live streaming, online gaming, and virtual conferencing has intensified the demand for robust and efficient data transmission over heterogeneous and dynamic access networks using a multipath streaming scheduler. Traditional model-based multipath schedulers lack adaptability. Although learning-based approaches promise improved adaptation, their centralized architectures introduce challenges in scalability, user privacy, and responsiveness. Furthermore, relying solely on learning at individual devices is often insufficient due to the limited volume and diversity of local data, making it challenging to derive robust and generalizable policies. To address these limitations, we propose Fed- MVP, a federated multi-homing video streaming protocol within a hierarchical cloud-edge architecture. Fed-MVP decentralizes learning and decision-making processes to edge data centers, enhancing scalability and preserving user privacy. It employs fine-tuned meta-models at the edge for real-time adaptation to local network conditions and utilizes Proximal Policy Optimization (PPO) for efficient learning. We introduce a Modified Federated Averaging (Mod-FedAvg) algorithm for effective aggregation of model updates from clients. Trace-driven emulations highlight Fed-MVP’s superior performance, demonstrating up to 61% and 43% improvement in convergence rate during test and training phases, respectively, up to 40% reduction in download time, up to 35% enhancement in video quality assessments, and up to 38% reduction in stalling time compared to state-of-the-art multipath schedulers.

As one of the key applications in future vehicular networks, extended sensor sharing (ESS) requires stringent quality-of-service (QoS) for disseminating sensed data to multiple vehicles under high mobility. Ensuring QoS for ESS is challenging network dynamics. To address this issue, we propose a QoS-guaranteed clustering and routing protocol (QCRP) to make routing decisions while adapting to network changes. Specifically, QCRP uses global network information to perform topology control to ensure network connectivity and find optimal routing paths which satisfy the QoS requirements. In addition, QCRP enables re-routing at each relay vehicle based on local network observations to quickly respond to network topology changes caused by mobility. We conduct simulations to evaluate the proposed routing protocol using traffic traces of different densities in a highway scenario and show our solution can achieve the design goal and outperform the existing state-of-the-art.