MobStream: A Reinforcement‐Driven Mobile Streaming Methodology Over Multipath QUIC

With the rapid growth of mobile communications, using mobile devices such as smartphones has become a trend. However, the limited uplink of the cellular network constrains the uploading quality. By integrating the link capacity of both cellular networks and WiFi, concurrent multipath transmission, such as multipath QUIC (MP‐QUIC), becomes a promising solution for alleviating the uplink bottleneck issue. This paper proposes MobStream, a novel reinforcement learning‐driven solution based on MP‐QUIC. MobStream maximizes streaming capacity by fully utilizing the bandwidth of both WiFi and cellular networks through MP‐QUIC. To address the issue of reduced performance caused by the differences in path quality between WiFi and cellular networks, MobStream incorporates partial reliability and a layered coding scheme, paving the way to adapt to varied network conditions without harming the bandwidth utilization. We formulate the concurrent transmission problem as a stochastic optimization task and demonstrate its solution using reinforcement learning methods. Furthermore, to ensure fairness in transmission among other single‐path protocols, we further introduced a fairness factor to the reinforcement learning method. Extensive experiments demonstrate that MobStream outperforms state‐of‐the‐art solutions in terms of bitrate, packet loss, and delay.