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In this paper we present a deep graph reinforcement learning model to predictand improve the user experience during a live video streaming event,orchestrated by an agent/tracker. We first formulate the user experienceprediction problem as a classification task, accounting for the fact that mostof the viewers at the beginning of an event have poor quality of experience dueto low-bandwidth connections and limited interactions with the tracker. In ourmodel we consider different factors that influence the quality of userexperience and train the proposed model on diverse state-action transitionswhen viewers interact with the tracker. In addition, provided that past eventshave various user experience characteristics we follow a gradient boostingstrategy to compute a global model that learns from different events. Ourexperiments with three real-world datasets of live video streaming eventsdemonstrate the superiority of the proposed model against several baselinestrategies. Moreover, as the majority of the viewers at the beginning of anevent has poor experience, we show that our model can significantly increasethe number of viewers with high quality experience by at least 75% over thefirst streaming minutes. Our evaluation datasets and implementation arepublicly available at https://publicresearch.z13.web.core.windows.net

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