An Algorithm for Transmitting VR Video Based on Adaptive Modulation
Jie Feng, Yongpeng Wu, Guangtao Zhai, Ning Liu, and Wenjun Zhang

TL;DR
This paper presents an adaptive modulation algorithm for VR video streaming that optimizes transmission by predicting user viewports, partitioning videos, and efficiently utilizing bandwidth to enhance user experience.
Contribution
It introduces a novel VR streaming strategy combining viewport prediction, convex optimization, and adaptive modulation for improved bandwidth efficiency.
Findings
Outperforms existing VR streaming algorithms in simulations
Enhances bandwidth efficiency through adaptive modulation
Improves user experience by optimizing video quality based on predicted viewports
Abstract
Virtual reality (VR) is making waves around the world recently. However, traditional video streaming is not suitable for VR video because of the huge size and view switch requirements of VR videos. Since the view of each user is limited, it is unnecessary to send the whole 360-degree scene at high quality which can be a heavy burden for the transmission system. Assuming filed-of-view (FoV) of each user can be predicted with high probability, we can divide the video screen into partitions and send those partitions which will appear in FoV at high quality. Hence, we propose an novel strategy for VR video streaming. First, we define a quality-of-experience metric to measure the viewing experience of users and define a channel model to reflect the fluctuation of the wireless channel. Next, we formulate the optimization problem and find its feasible solution by convex optimization. In order…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Visual Attention and Saliency Detection
