Work

LCR360: Efficient Head Movement Prediction and Viewport Sharing in 360° Video Streaming

Year: 2024

Type: conference-paper

Abstract: In the realm of 360° video streaming, three paramount challenges arise: accurate prediction of the field of view, efficient bandwidth utilization, and seamless viewport sharing. Current solutions often neglect the latter aspect of 360° videos, thereby necessitating ultra-low latency, high quality, and real-time rendering. The considerable size of 360° videos further intensifies these challenges. To tackle these issues, we introduce LCR360, a deep learning-based neural network solution designed for accurate head movement prediction. Our solution is deployed on E3PO [9], an end-to-end open-source evaluation platform dedicated to 360° video-on-demand streaming. By integrating simulated streams into the Unreal Engine, we provide an immersive experience that enables users to share viewports seamlessly. Moreover, LCR360 surpasses its competitors by ~5% in the S-metric, a measure that combines the MSE of video quality, bandwidth, and storage costs.

Source: Proceedings of the 28th Annual International Conference on Mobile Computing And Networking

DOI: 10.1145/3636534.3696210

Authors: Jashanjot Singh Sidhu, Abdelhak Bentaleb

Institution: Concordia University

Language: English

Topic: Image and Video Quality Assessment

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Natural Sciences and Engineering Research Council of Canada

Award: G4998900716