An Adaptation Aware Model to Predict Engagement on HTTP Adaptive Live Streaming

Today, video streaming is a well-established application on the internet. Providers are able of transmitting video globally, in large-scale, although there are still some challenges, such as offering high video quality for all clients, particularly in live transmission, due to limited buffer size and intense client arrival. Low quality result in low engagement (i.e. client permanence). To increase engagement, the provider can adopt strategies to anticipate (i.e. predict) quality losses. To make this possible, the provider needs to learn what session characteristics lead to an early session abandonment. If a new session presents such characteristics, the provider can presume that it will terminate prematurely. To implement an accurate prediction scheme we must choose a feature set able to explain the user permanence (i.e. engagement). Traditional prediction approaches use user-centered quality metrics such stall rate and average bitrate. In our data-set, these metrics give a prediction accuracy of less than 70%. To improve this, we propose a new set of metrics that explores the client adaptation flow. We model it through Markov chains and show that this increases the prediction accuracy up to 82%. Besides this, grouping similar sessions and train a predictor for each group can increase the accuracy. Finally, we present a use case for the predictor in order to investigate its feasibility in real systems.