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arXiv 2107.13619cs.AIcs.LG
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一种用于改善流媒体直播用户体验的深度图强化学习模型

A Deep Graph Reinforcement Learning Model for Improving User Experience in Live Video Streaming

KTH Royal Institute of Technology（KTH皇家理工学院）；Hive Streaming AB（Hive流媒体公司）；University of Thessaly（色萨利大学）

Stefanos Antaris, Dimitrios Rafailidis, Sarunas Girdzijauskas

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摘要：

在本文中，我们提出了一个深度图强化学习模型，以预测和改善用户体验在直播视频流事件，精心策划的代理/跟踪器。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有限交互，事件开始时大多数观众的体验质量较差。在我们的模型中，我们考虑不同的因素，影响用户体验的质量和训练不同的状态动作转换时，观众与跟踪器交互的模型。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学习的全局模型。我们的实验与实时视频流事件的三个真实世界的数据集证明了所提出的模型对几个基线策略的优越性。此外，由于大多数观众在活动开始时体验不佳，我们表明我们的模型可以显着增加具有高质量体验的观众数量，在第一个流媒体分钟内至少增加75%。我们的评估数据集和实施可在https://publicresearch.z13.web.core.windows.net上公开获取

英文摘要：

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

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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