# Structured Reinforcement Learning for Media Streaming at the Wireless Edge

arXiv: https://arxiv.org/abs/2404.07315 · HTML: https://mlsanity.com/paper/2404.07315/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 13 of its 48 references that are in the map.
- First posted on arXiv 10 Apr 2024.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Constrained Policy Optimization](https://mlsanity.com/paper/1705.10528/) (2017)
- [Constrained Reinforcement Learning Has Zero Duality Gap](https://mlsanity.com/paper/1910.13393/) (2019)
- [Exploration-Exploitation in Constrained MDPs](https://mlsanity.com/paper/2003.02189/) (2020)
- [Provably Efficient Safe Exploration via Primal-Dual Policy Optimization](https://mlsanity.com/paper/2003.00534/) (2020)
- [Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs](https://mlsanity.com/paper/2106.02684/) (2021)
- [Near-Optimal Sample Complexity Bounds for Constrained MDPs](https://mlsanity.com/paper/2206.06270/) (2022)
- [DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning](https://mlsanity.com/paper/2112.00885/) (2021)
- [A Sample-Efficient Algorithm for Episodic Finite-Horizon MDP with Constraints](https://mlsanity.com/paper/2009.11348/) (2020)
- [Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs](https://mlsanity.com/paper/2008.00311/) (2020)
- [Online Reinforcement Learning of Optimal Threshold Policies for Markov Decision Processes](https://mlsanity.com/paper/1912.10325/) (2019)
- [Inexact First-Order Primal-Dual Algorithms](https://mlsanity.com/paper/1803.10576/) (2018)
- [Optimal Decentralized Dynamic Policies for Video Streaming over Wireless Channels](https://mlsanity.com/paper/1902.07418/) (2019)

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

- [ASL360: AI-Enabled Adaptive Streaming of Layered 360^circ Video over UAV-assisted Wireless Networks](https://mlsanity.com/paper/2509.10544/) (2025)

## Abstract (arXiv)

Media streaming is the dominant application over wireless edge (access) networks. The increasing softwarization of such networks has led to efforts at intelligent control, wherein application-specific actions may be dynamically taken to enhance the user experience. The goal of this work is to develop and demonstrate learning-based policies for optimal decision making to determine which clients to dynamically prioritize in a video streaming setting. We formulate the policy design question as a constrained Markov decision problem (CMDP), and observe that by using a Lagrangian relaxation we can decompose it into single-client problems. Further, the optimal policy takes a threshold form in the video buffer length, which enables us to design an efficient constrained reinforcement learning (CRL) algorithm to learn it. Specifically, we show that a natural policy gradient (NPG) based algorithm that is derived using the structure of our problem converges to the globally optimal policy. We then develop a simulation environment for training, and a real-world intelligent controller attached to a WiFi access point for evaluation. We empirically show that the structured learning approach enables fast learning. Furthermore, such a structured policy can be easily deployed due to low computational complexity, leading to policy execution taking only about 15μs. Using YouTube streaming experiments in a resource constrained scenario, we demonstrate that the CRL approach can increase quality of experience (QOE) by over 30%.