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Video QoE Inference with Machine Learning

机译：视频QoE推论机器学习

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摘要

HTTP adaptive streaming (HAS) has become the de-facto standard for delivering video over the Internet. More content providers like YouTube and Twitch have started generating and delivering high quality streams (usually 4k resolution) with advanced end-to-end encryption mechanisms. This huge increase in HAS encrypted traffic, creates a significant challenge for network providers in understanding what is happening on their infrastructures which limits their ability to manage network infrastructures properly. Due to such invisibility, the network providers could not take appropriate decisions for better optimizations, resulting in significant revenue lost. Inferring the quality of experience (QoE) of HAS-based streaming video services is important, but recent studies highlight that most of existing solutions that rely on packet inspections, showing low performance in inference accuracy. To address this issue, we develop a machine learning powered system that infers QoE factors such as startup delay, rebuffering and selected quality, for encrypted on-demand HAS streaming video services. Our solution uses two data-driven techniques: Deep Self Organizing Map (DSOM) and Multi Layer Perceptron Backpropagation (MLPB), allowing efficient accuracy with low error in inferring QoE factors over several public video datasets, compared to some state-of-the-art approaches.
机译：HTTP自适应流（具有）已成为通过Internet提供视频的De-Facto标准。与YouTube和Twitch等更多内容提供商已经开始使用高级端到端加密机制生成和提供高质量的流（通常是4K分辨率）。加密流量的巨大增加，为网络提供商创造了一个重​​大挑战，了解他们的基础架构发生的事情，这限制了它们正确管理网络基础架构的能力。由于这种隐形，网络提供商无法采取适当的决策以更好地优化，从而损失了重大收入。推断基于物流视频服务的经验质量（QoE）是重要的，但最近的研究突出了大多数依赖于数据包检查的现有解决方案，表现出推理准确性的低性能。为了解决这个问题，我们开发了一种机器学习动力系统，即在启动延迟，反对和选择的质量等QoE因素，用于加密的按需具有流式视频服务。我们的解决方案使用两个数据驱动技术：深度自组织地图（DSOM）和多层Perceptron BackPropagation（MLPB），允许在几个公共视频数据集中推断出Qoe因素的低误差，而允许有效的准确性，相比于某些状态 - 艺术方法。

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