QoS-aware Reinforcement Learning for Multimedia Traffic Scheduling in Home Area Networks

Cloud-based interactive multimedia applications such as virtual games and video streaming are gaining high popularity. However, giving the high bandwidth consumption, the remote execution can negatively impact the quality of the multimedia traffic. In such a realm, data travel different communication networks from the cloud to the final users crossing the last meters the home's access point (AP). In such a scenario, the quality-of-service (QoS) support is a challenging task, particularly in the home network environment, with heterogeneous applications simultaneously running and consuming the available bandwidth. To address this issue, we propose ReiLeCS, a Reinforcement Learning-based Controller and Scheduler for interactive multimedia traffic in Home Area Networks (HAN). Through reinforcement learning and the maximization of a reward function, it enables the AP to schedule the arriving multimedia traffic from the cloud according to their required QoS. Simulation results using real multimedia traffic conditions demonstrate that ReiLeCS achieves better performances compared with existing packet scheduling policies.