Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery

TL;DR

This paper introduces a novel Actor-Critic reinforcement learning scheduler for real-time multimedia delivery over multipath wireless systems, specifically targeting UAV video streaming, which adapts dynamically to network conditions without prior training.

Contribution

It presents a new RL-based scheduler that learns optimal path selection and redundancy in real-time for UAV multimedia streaming, without requiring prior network model knowledge.

Findings

Achieves very low loss rates in simulations

Adapts dynamically to changing network conditions

Operates in real-time without prior training

Abstract

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.