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Optimized Upload Strategies for Live Scalable Video Transmission from Mobile Devices

机译:用于从移动设备进行实时可扩展视频传输的优化上传策略

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摘要

Sharing live multimedia content is becoming increasingly popular among mobile users. In this article, we study the problem of optimizing video quality in such a scenario using scalable video coding (SVC) and chunked video content. We consider using only standard stateless HTTP servers that do not need to perform additional processing of the video content. Our key contribution is to provide close to optimal algorithms for scheduling video chunk upload for multiple clients having different viewing delays. Given such a set of clients, the problem is to decide which chunks to upload and in which order to upload them so that the quality-delay tradeoff can be optimally balanced. We show by means of simulations that the proposed algorithms can achieve notably better performance than naive solutions in practical cases. Especially the heuristic-based greedy algorithm is a good candidate for deployment on mobile devices because it is not computationally intensive but it still delivers in most cases on-par video qualitycompared to the more complex local optimization algorithm. We also show that using shorter video segments and being able to predict bandwidth and video chunk properties improve the delivered video quality in certain cases.
机译:在移动用户中,共享实时多媒体内容变得越来越流行。在本文中,我们研究使用可伸缩视频编码(SVC)和分块视频内容在这种情况下优化视频质量的问题。我们考虑只使用不需要执行视频内容附加处理的标准无状态HTTP服务器。我们的主要贡献是为具有不同观看延迟的多个客户端提供接近最佳的算法来安排视频块上传。给定这样的一组客户,问题在于确定要上载哪些块以及以什么顺序上载它们,以便可以最佳地平衡质量延迟权衡。我们通过仿真表明,在实际情况下,所提出的算法可以比单纯的解决方案显着提高性能。尤其是基于启发式的贪婪算法是移动设备上部署的一个很好的候选者,因为它不占用大量计算资源,但与更复杂的局部优化算法相比,它在大多数情况下仍可提供同等的视频质量。我们还表明,在某些情况下,使用较短的视频段并能够预测带宽和视频块属性可以改善交付的视频质量。

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