Learning Based Scheduling and Adaptive Congestion Control for Multipath QUIC

As the number of devices with multiple communication interfaces increases, the connection redundancy is being considered for efficient bandwidth utilization and improved reliability. Accordingly, multipath transport technologies are attracting attention. This paper investigates the Multipath Quick UDP Internet Connection (MPQUIC) transport protocol with the goal of enhancing data throughput and packet transmission efficiency. We introduce a novel Blocking Probability (BLP) path scheduler, which leverages a learned blocking threshold for probability-based blocking decision-making, and an innovative congestion controller, which not only dynamically adjusts congestion window size but also optimizes packet size. Our simulations demonstrate that BLP significantly improves network throughput, packet delay time, and overall transmission efficiency in both homogeneous and heterogeneous network environments. By outperforming benchmark schedulers, BLP showcases the potential of advanced scheduling strategies to adapt to network dynamics, ensuring reliable and efficient data delivery.