In this paper we present a deep graph reinforcement learning model to predict and improve the user experience during a live video streaming event, orchestrated by an agent/tracker. We first formulate the user experience prediction problem as a classification task, accounting for the fact that most of the viewers at the beginning of an event have poor quality of experience due to low-bandwidth connections and limited interactions with the tracker. In our model we consider different factors that influence the quality of user experience and train the proposed model on diverse state-action transitions when viewers interact with the tracker. In addition, provided that past events have various user experience characteristics we follow a gradient boosting strategy to compute a global model that learns from different events. Our experiments with three real-world datasets of live video streaming events demonstrate the superiority of the proposed model against several baseline strategies. Moreover, as the majority of the viewers at the beginning of an event has poor experience, we show that our model can significantly increase the number of viewers with high quality experience by at least 75% over the first streaming minutes. Our evaluation datasets and implementation are publicly available at https://publicresearch.z13.web.core.windows.net

中文翻译：

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用于改善实时视频流中的用户体验的深度图强化学习模型

在本文中，我们提出了一种深度图强化学习模型，用于在由代理/跟踪器编排的实时视频流事件期间预测和改善用户体验。我们首先将用户体验预测问题制定为分类任务，考虑到由于低带宽连接和与跟踪器的交互有限，大多数观众在事件开始时的体验质量较差。在我们的模型中，我们考虑了影响用户体验质量的不同因素，并在观众与跟踪器交互时针对不同的状态-动作转换训练所提出的模型。此外，假设过去的事件具有各种用户体验特征，我们遵循梯度提升策略来计算从不同事件中学习的全局模型。我们对实时视频流事件的三个​​真实世界数据集的实验证明了所提出的模型相对于几种基线策略的优越性。此外，由于事件开始时的大多数观众体验不佳，我们表明我们的模型可以在流媒体的前几分钟内将具有高质量体验的观众数量显着增加至少 75%。我们的评估数据集和实施可在 https://publicresearch.z13.web.core.windows.net 上公开获得 我们表明，我们的模型可以在流媒体的前几分钟内将具有高质量体验的观众数量显着增加至少 75%。我们的评估数据集和实施可在 https://publicresearch.z13.web.core.windows.net 上公开获得 我们表明，我们的模型可以在流媒体的前几分钟内将具有高质量体验的观众数量显着增加至少 75%。我们的评估数据集和实施可在 https://publicresearch.z13.web.core.windows.net 上公开获得