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[2509.10544] ASL360: AI-Enabled Adaptive Streaming of Layered 360$^\circ$ Video over UAV-assisted Wireless Networks
Summary
The paper presents ASL360, an AI-based system for adaptive streaming of layered 360° video over UAV-assisted wireless networks, enhancing user experience in mobile VR.
Why It Matters
With the rise of immersive technologies like virtual reality, optimizing video streaming quality is crucial. ASL360 addresses challenges in delivering high-quality 360° video in dynamic environments, making it relevant for future wireless communication and multimedia applications.
Key Takeaways
- ASL360 utilizes deep reinforcement learning for video streaming optimization.
- The system significantly improves user Quality of Experience (QoE) metrics.
- It employs a layered video encoding strategy to enhance streaming efficiency.
- Dynamic adjustments based on real-time conditions are integral to its performance.
- The approach is particularly effective in challenging network environments.
Computer Science > Networking and Internet Architecture arXiv:2509.10544 (cs) [Submitted on 7 Sep 2025 (v1), last revised 21 Feb 2026 (this version, v2)] Title:ASL360: AI-Enabled Adaptive Streaming of Layered 360$^\circ$ Video over UAV-assisted Wireless Networks Authors:Alireza Mohammadhosseini, Jacob Chakareski, Nicholas Mastronarde View a PDF of the paper titled ASL360: AI-Enabled Adaptive Streaming of Layered 360$^\circ$ Video over UAV-assisted Wireless Networks, by Alireza Mohammadhosseini and 2 other authors View PDF HTML (experimental) Abstract: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 adjus...
