# ASL360: AI-Enabled Adaptive Streaming of Layered 360^circ Video over UAV-assisted Wireless Networks

arXiv: https://arxiv.org/abs/2509.10544 · HTML: https://mlsanity.com/paper/2509.10544/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 7 Sep 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Towards Low-Latency and Ultra-Reliable Virtual Reality](https://mlsanity.com/paper/1801.07587/) (2018)
- [Viewport-Adaptive Navigable 360-Degree Video Delivery](https://mlsanity.com/paper/1609.08042/) (2016)
- [Adaptive 360 VR Video Streaming: Divide and Conquer!](https://mlsanity.com/paper/1609.08729/) (2016)
- [Structured Reinforcement Learning for Media Streaming at the Wireless Edge](https://mlsanity.com/paper/2404.07315/) (2024)
- [Multi-Task Decision-Making for Multi-User 360 Video Processing over Wireless Networks](https://mlsanity.com/paper/2407.03426/) (2024)

## Abstract (arXiv)

We propose ASL360, an adaptive deep reinforcement learning-based scheduler for on-demand 360^circ video streaming to mobile VR users in next generation wireless networks. We aim to maximize the overall Quality of Experience (QoE) of the users served over a UAV-assisted 5G wireless network. Our system model comprises a macro base station (MBS) and a UAV-mounted base station which both deploy mm-Wave transmission to the users. The 360^circ video is encoded into dependent layers and segmented tiles, allowing a user to schedule downloads of each layer's segments. Furthermore, each user utilizes multiple buffers to store the corresponding video layer's segments. We model the scheduling decision as a Constrained Markov Decision Process (CMDP), where the agent selects Base or Enhancement layers to maximize the QoE and use a policy gradient-based method (PPO) to find the optimal policy. Additionally, we implement a dynamic adjustment mechanism for cost components, allowing the system to adaptively balance and prioritize the video quality, buffer occupancy, and quality change based on real-time network and streaming session conditions. We demonstrate that ASL360 significantly improves the QoE, achieving approximately 2 dB higher average video quality, 80% lower average rebuffering time, and 57% lower video quality variation, relative to competitive baseline methods. Our results show the effectiveness of our layered and adaptive approach in enhancing the QoE in immersive videostreaming applications, particularly in dynamic and challenging network environments.