  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

Artificial Intelligence;Machine Learning