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 standards-compliant components and provide a comprehensive performance evaluation of Mosaic along with five other streaming techniques – two for conventional adaptive video streaming and three for 360-degree tile-based video streaming. Mosaic outperforms the best of the competitions by as much as 47-191% in terms of average video quality of experience. Simulation-based evaluation as well as subjective user studies further confirm the superiority of the proposed approach.

中文翻译：

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马赛克：通过机器学习提高用户在360度视频流中的体验质量

用于流传输360度全景视频的常规流传输解决方案效率低下，因为它们会下载整个360度全景场景，而用户只能查看场景的一小部分，称为视口。这会浪费超过80％的网络带宽。我们开发了一种综合方法，称为马赛克结合了强大的基于神经网络的视口预测和速率控制机制，该机制将速率分配给360度帧中的不同图块，从而根据给定的网络容量优化视频体验。我们将优化模型建模为多选背包问题，并使用贪婪方法进行求解。我们还使用符合标准的组件开发了一个端到端测试平台，并提供了全面的性能评估。马赛克 以及其他五种流传输技术–两种用于常规自适应视频流传输，另外三种用于360度基于图块的视频流传输。 马赛克就平均视频体验质量而言，其表现要比同类最佳竞赛高出47-191％。基于仿真的评估以及主观的用户研究进一步证实了所提出方法的优越性。