Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery
This addresses the problem of reliable multimedia delivery in UAV-based multipath systems, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles real-time video streaming from a UAV using multiple wireless paths by developing a scheduler based on an Actor-Critic RL algorithm, which dynamically adapts to path conditions to achieve a very low loss rate at the receiver without prior network knowledge.
Reinforcement Learning (RL) has recently found wide applications in network traffic management and control because some of its variants do not require prior knowledge of network models. In this paper, we present a novel scheduler for real-time multimedia delivery in multipath systems based on an Actor-Critic (AC) RL algorithm. We focus on a challenging scenario of real-time video streaming from an Unmanned Aerial Vehicle (UAV) using multiple wireless paths. The scheduler acting as an RL agent learns in real-time the optimal policy for path selection, path rate allocation and redundancy estimation for flow protection. The scheduler, implemented as a module of the GStreamer framework, can be used in real or simulated settings. The simulation results show that our scheduler can target a very low loss rate at the receiver by dynamically adapting in real-time the scheduling policy to the path conditions without performing training or relying on prior knowledge of network channel models.