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

arXiv: https://arxiv.org/abs/2204.13343 · HTML: https://mlsanity.com/paper/2204.13343/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 28 Apr 2022.

## Builds on (most cited first, 2 of 2)

- [A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access](https://mlsanity.com/paper/1908.08401/) (2019)
- [A Holistic Survey of Wireless Multipath Video Streaming](https://mlsanity.com/paper/1906.06184/) (2019)

## Built on by (most cited first, 1 of 1)

- [Handover and SINR-Aware Path Optimization in 5G-UAV mmWave Communication using DRL](https://mlsanity.com/paper/2504.02688/) (2025)

## Abstract (arXiv)

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.