Live video streaming has seen tremendous growth in the past decade. An important fact in live streaming is that the demand for low playback-latency inherently conflicts with the desire for high QoE. This requires different types of live services to seek different latency-QoE tradeoffs according to their service-requirements. However, our investigations revealed that it is fundamentally difficult for existing streaming algorithms to keep consistent latency in changing network conditions, let alone achieve the service-desired latency-QoE tradeoff. To tackle the challenge, this article develops a novel framework called Flexible Latency Aware Streaming (FLAS) that not only can achieve consistent low latency, but also control the latency-QoE tradeoff flexibly. Specifically, FLAS generates a set of adaptation logics offline, each optimized for a candidate tradeoff point, then selects the most appropriate one to run online. We first show how FLAS can be applied to optimizing the existing algorithms, then developed a novel Genetic Programming approach to fully exploit FLAS's potential. Extensive evaluations show that FLAS can precisely control latency all the way down to 1s and achieve substantially higher QoE than state-of-the-arts. FLAS can be readily implemented into real streaming platforms, offering a practical and reliable solution for live-streaming services.

中文翻译：

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实时视频流中灵活播放延迟控制的统一框架

在过去的十年中，实时视频流取得了巨大的增长。直播流中的一个重要事实是，对低播放延迟的需求与对高 QoE 的需求存在内在冲突。这需要不同类型的实时服务根据其服务需求寻求不同的延迟 QoE 权衡。然而，我们的调查显示，现有的流媒体算法从根本上很难在不断变化的网络条件下保持一致的延迟，更不用说实现服务所需的延迟 - QoE 权衡了。为了应对这一挑战，本文开发了一种称为灵活延迟感知流媒体 (FLAS) 的新颖框架，该框架不仅可以实现一致的低延迟，还可以灵活地控制延迟与 QoE 的权衡。具体来说，FLAS离线生成一组适配逻辑，每个都针对候选权衡点进行了优化，然后选择最合适的一个在线运行。我们首先展示了如何将 FLAS 应用于优化现有算法，然后开发了一种新颖的遗传编程方法来充分利用 FLAS 的潜力。广泛的评估表明，FLAS 可以将延迟精确控制到 1 秒，并实现比最先进技术高得多的 QoE。FLAS 可以很容易地实现到真实的流媒体平台中，为直播服务提供实用且可靠的解决方案。广泛的评估表明，FLAS 可以将延迟精确控制到 1 秒，并实现比最先进技术高得多的 QoE。FLAS 可以很容易地实现到真实的流媒体平台中，为直播服务提供实用且可靠的解决方案。广泛的评估表明，FLAS 可以将延迟精确控制到 1 秒，并实现比最先进技术高得多的 QoE。FLAS 可以很容易地实现到真实的流媒体平台中，为直播服务提供实用且可靠的解决方案。