Viewport-Driven Rate-Distortion Optimized 360{\deg} Video Streaming
Jacob Chakareski, Ridvan Aksu, Xavier Corbillon, Gwendal Simon, and, Viswanathan Swaminathan

TL;DR
This paper presents a viewport-driven rate-distortion optimization framework for 360-degree video streaming that enhances user experience by efficiently allocating resources based on user navigation patterns and content characteristics.
Contribution
It introduces a novel framework combining user navigation heat maps, spatiotemporal rate-distortion analysis, and an optimization model for improved 360-degree video streaming quality.
Findings
Achieved 4-5 dB quality gains on 4K 360 videos.
Demonstrated advantages over conventional uniform encoding methods.
Validated effectiveness through experimental results.
Abstract
The growing popularity of virtual and augmented reality communications and 360{\deg} video streaming is moving video communication systems into much more dynamic and resource-limited operating settings. The enormous data volume of 360{\deg} videos requires an efficient use of network bandwidth to maintain the desired quality of experience for the end user. To this end, we propose a framework for viewport-driven rate-distortion optimized 360{\deg} video streaming that integrates the user view navigation pattern and the spatiotemporal rate-distortion characteristics of the 360{\deg} video content to maximize the delivered user quality of experience for the given network/system resources. The framework comprises a methodology for constructing dynamic heat maps that capture the likelihood of navigating different spatial segments of a 360{\deg} video over time by the user, an analysis and…
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Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Advanced Vision and Imaging
