Optimal Strategies for Live Video Streaming in the Low-latency Regime

Liyang Sun, Tongyu Zong, Yong Liu, Yao Wang, Haihong Zhu
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引用次数: 13

Abstract

Low-latency is a critical user Quality-of-Experience (QoE) metric for live video streaming. It poses significant challenges for streaming over the Internet. In this paper, we explore the design space of low-latency live video streaming by developing dynamic models and optimal control strategies. We further develop practical live video streaming algorithms within the Model Predictive Control (MPC) framework, namely MPC-Live, to maximize user QoE by adapting the video bitrate while maintaining low end-to-end video latency in dynamic network environment. Through extensive experiments driven by real network traces, we demonstrate that our live video streaming algorithms can improve the performance dramatically within latency range of two to five seconds.
低延迟实时视频流的最优策略
低延迟是实时视频流的关键用户体验质量(QoE)指标。这给互联网上的流媒体带来了巨大的挑战。在本文中,我们通过建立动态模型和最优控制策略来探索低延迟直播视频流的设计空间。我们进一步在模型预测控制(MPC)框架内开发实用的实时视频流算法,即MPC- live,在动态网络环境中通过调整视频比特率同时保持低端到端视频延迟来最大化用户QoE。通过真实网络轨迹驱动的大量实验,我们证明了我们的实时视频流算法可以在2到5秒的延迟范围内显着提高性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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