Advancing user quality of experience in 360-degree video streaming

Abstract

Conventional streaming solutions for streaming 360- degree panoramic videos are inefficient in that they download the entire 360-degree panoramic scene, while the user views only a small sub-part of the scene called the viewport. This can waste over 80% of the network bandwidth. We develop a comprehensive approach called Mosaic that combines a powerful neural network-based viewport prediction with a rate control mechanism that assigns rates to different tiles in the 360-degree frame such that the video quality of experience is optimized subject to a given network capacity. We model the optimization as a multi-choice knapsack problem and solve it using a greedy approach.We also develop an end-to-end testbed using standardscompliant components and provide a comprehensive performance evaluation of Mosaic along with four other streaming techniques – two for conventional adaptive video streaming and two for 360- degree tile-based video streaming. Mosaic outperforms the best of the competition by as much as 50% in terms of median video quality.