# Multi-Task Decision-Making for Multi-User 360 Video Processing over Wireless Networks

arXiv: https://arxiv.org/abs/2407.03426 · HTML: https://mlsanity.com/paper/2407.03426/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 3 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Mastering Complex Control in MOBA Games with Deep Reinforcement Learning](https://mlsanity.com/paper/1912.09729/) (2019)
- [Phasic Policy Gradient](https://mlsanity.com/paper/2009.04416/) (2020)
- [BOLA: Near-Optimal Bitrate Adaptation for Online Videos](https://mlsanity.com/paper/1601.06748/) (2016)
- [BONES: Near-Optimal Neural-Enhanced Video Streaming](https://mlsanity.com/paper/2310.09920/) (2023)
- [Towards Enabling Next Generation Societal Virtual Reality Applications for Virtual Human Teleportation](https://mlsanity.com/paper/2208.04998/) (2022)
- [Exploring the Interplay of Interference and Queues in Unlicensed Spectrum Bands for UAV Networks](https://mlsanity.com/paper/2308.05187/) (2023)
- [Energy-Efficient Deadline-Aware Edge Computing: Bandit Learning with Partial Observations in Multi-Channel Systems](https://mlsanity.com/paper/2308.06647/) (2023)
- [QoE-Centric Multi-User mmWave Scheduling: A Beam Alignment and Buffer Predictive Approach](https://mlsanity.com/paper/2207.00532/) (2022)

## 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)

We study a multi-task decision-making problem for 360 video processing in a wireless multi-user virtual reality (VR) system that includes an edge computing unit (ECU) to deliver 360 videos to VR users and offer computing assistance for decoding/rendering of video frames. However, this comes at the expense of increased data volume and required bandwidth. To balance this trade-off, we formulate a constrained quality of experience (QoE) maximization problem in which the rebuffering time and quality variation between video frames are bounded by user and video requirements. To solve the formulated multi-user QoE maximization, we leverage deep reinforcement learning (DRL) for multi-task rate adaptation and computation distribution (MTRC). The proposed MTRC approach does not rely on any predefined assumption about the environment and relies on video playback statistics (i.e., past throughput, decoding time, transmission time, etc.), video information, and the resulting performance to adjust the video bitrate and computation distribution. We train MTRC with real-world wireless network traces and 360 video datasets to obtain evaluation results in terms of the average QoE, peak signal-to-noise ratio (PSNR), rebuffering time, and quality variation. Our results indicate that the MTRC improves the users' QoE compared to state-of-the-art rate adaptation algorithm. Specifically, we show a 5.97 dB to 6.44 dB improvement in PSNR, a 1.66X to 4.23X improvement in rebuffering time, and a 4.21 dB to 4.35 dB improvement in quality variation.