Cross Layer Optimization and Distributed Reinforcement Learning for Wireless 360° Video Streaming

• URL: http://arxiv.org/abs/2011.06356v3
• Date: Tue, 24 Sep 2024 07:55:21 GMT
• Title: Cross Layer Optimization and Distributed Reinforcement Learning for Wireless 360° Video Streaming
• Authors: Anis Elgabli, Mohammed S. Elbamby, Cristina Perfecto, Mounssif Krouka, Mehdi Bennis, Vaneet Aggarwal,
• Abstract summary: We propose a cross layer optimization approach that maximizes the available rate to each user and efficiently uses it to maximize users' QoE. We show that the problem can be decoupled into two interrelated subproblems. An actor-critic deep reinforcement learning (DRL) is proposed to leverage the parallel training of multiple independent agents and solve the application layer subproblem.
• Score: 54.60967639512643
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Wirelessly streaming high quality 360 degree videos is still a challenging problem. When there are many users watching different 360 degree videos and competing for the computing and communication resources, the streaming algorithm at hand should maximize the average quality of experience (QoE) while guaranteeing a minimum rate for each user. In this paper, we propose a cross layer optimization approach that maximizes the available rate to each user and efficiently uses it to maximize users' QoE. Particularly, we consider a tile based 360 degree video streaming, and we optimize a QoE metric that balances the tradeoff between maximizing each user's QoE and ensuring fairness among users. We show that the problem can be decoupled into two interrelated subproblems: (i) a physical layer subproblem whose objective is to find the download rate for each user, and (ii) an application layer subproblem whose objective is to use that rate to find a quality decision per tile such that the user's QoE is maximized. We prove that the physical layer subproblem can be solved optimally with low complexity and an actor-critic deep reinforcement learning (DRL) is proposed to leverage the parallel training of multiple independent agents and solve the application layer subproblem. Extensive experiments reveal the robustness of our scheme and demonstrate its significant performance improvement compared to several baseline algorithms.

Related papers

• Multi-Task Decision-Making for Multi-User 360 Video Processing over Wireless Networks [9.642593500545997]
  We study a multi-task decision-making problem for 360 video processing in a wireless multi-user virtual reality (VR) system. This comes at the expense of increased data volume and required bandwidth. We propose a constrained quality of experience (QoE) problem in which the rebuffering time and quality variation between video frames are rely by user and video requirements.
  arXiv  Detail & Related papers  (2024-07-03T18:09:25Z)
• MADRL-Based Rate Adaptation for 360° Video Streaming with Multi-Viewpoint Prediction [3.8611070161950916]
  A key challenge of 360deg video playback is ensuring a high quality of experience (QoE) with limited network bandwidth. Currently, most studies focus on tile-based adaptive (ABR) streaming based on single viewport prediction to reduce bandwidth consumption. This paper first presents a multimodal spatial-temporal attention transformer to generate multiple viewpoint trajectories with their probabilities given a historical trajectory. After that, a multi-agent deep reinforcement learning (MADRL)-based ABR algorithm utilizing multi-viewpoint prediction for 360deg video streaming is proposed.
  arXiv  Detail & Related papers  (2024-05-13T13:59:59Z)
• Efficient Controllable Multi-Task Architectures [85.76598445904374]
  We propose a multi-task model consisting of a shared encoder and task-specific decoders where both encoder and decoder channel widths are slimmable. Our key idea is to control the task importance by varying the capacities of task-specific decoders, while controlling the total computational cost. This improves overall accuracy by allowing a stronger encoder for a given budget, increases control over computational cost, and delivers high-quality slimmed sub-architectures.
  arXiv  Detail & Related papers  (2023-08-22T19:09:56Z)
• Total Variation Optimization Layers for Computer Vision [130.10996341231743]
  We propose total variation (TV) minimization as a layer for computer vision. Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets. We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising.
  arXiv  Detail & Related papers  (2022-04-07T17:59:27Z)
• Low-Latency Federated Learning over Wireless Channels with Differential Privacy [142.5983499872664]
  In federated learning (FL), model training is distributed over clients and local models are aggregated by a central server. In this paper, we aim to minimize FL training delay over wireless channels, constrained by overall training performance as well as each client's differential privacy (DP) requirement.
  arXiv  Detail & Related papers  (2021-06-20T13:51:18Z)
• Feeling of Presence Maximization: mmWave-Enabled Virtual Reality Meets Deep Reinforcement Learning [76.46530937296066]
  This paper investigates the problem of providing ultra-reliable and energy-efficient virtual reality (VR) experiences for wireless mobile users. To ensure reliable ultra-high-definition (UHD) video frame delivery to mobile users, a coordinated multipoint (CoMP) transmission technique and millimeter wave (mmWave) communications are exploited.
  arXiv  Detail & Related papers  (2021-06-03T08:35:10Z)
• Distributed Deep Reinforcement Learning for Collaborative Spectrum Sharing [29.23509739013885]
  We discuss the problem of distributed spectrum collaboration without central management under general unknown channels. We combine game-theoretic insights with deep Q-learning to provide a novelally optimal solution to the spectrum collaboration problem.
  arXiv  Detail & Related papers  (2021-04-06T04:33:06Z)
• Real-world Video Adaptation with Reinforcement Learning [38.26695924173461]
  Client-side video players employ adaptive (ABR) algorithms to optimize user quality of experience (QoE) We evaluate recently proposed RL-based ABR methods in Facebook's web-based video streaming platform. In a week-long worldwide deployment with more than 30 million video streaming sessions, our RL approach outperforms the existing human-engineered ABR algorithms.
  arXiv  Detail & Related papers  (2020-08-28T21:44:24Z)
• Non-Cooperative Game Theory Based Rate Adaptation for Dynamic Video Streaming over HTTP [89.30855958779425]
  Dynamic Adaptive Streaming over HTTP (DASH) has demonstrated to be an emerging and promising multimedia streaming technique. We propose a novel algorithm to optimally allocate the limited export bandwidth of the server to multi-users to maximize their Quality of Experience (QoE) with fairness guaranteed.
  arXiv  Detail & Related papers  (2019-12-27T01:19:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.