Improving the QoE of OTT Multimedia Services in Wireless Scenarios One of the goals of an operator is to improve the Quality of Experience (QoE) of a client in networks where Overthetop (OTT) content is being delivered, as the Internet has now become the xe2x80x98de factoxe2x80x98 path for video transmission, with video data representing over 75% of the entire network traffic, and the usersxe2x80x99 desires to consume more content continuing to show exponential growth. This paper proposes a new approach to improve the clientsxe2x80x99 QoE by using network awareness models as a way to forecast the number of future video content chunks to be cached. Once cached, these chunks become closer to the client, which mitigates the consequences of suboptimal network performance on bitrateadaptive HTTP video streaming. Two prefetching approaches are proposed: an Analytic Hierarchy Process (AHP) through a weighted function on the measured quality of service metrics, and a Neural Network to estimate and maximize the user experience without the normal service delivery degradation. Our evaluation shows that AHP provides a suboptimal behavior under constrained bandwidth, while a neural network model is able to infer network behavior and predict an optimized MOS, allowing it to better optimize the caching system and the required prefetching to adequately improve QoE.

An Adaptation Aware Model to Predict Engagement on HTTP Adaptive Live Streaming Today, video streaming is a wellestablished application on the internet. Providers are able of transmitting video globally, in largescale, although there are still some challenges, such as offering high video quality for all clients, particularly in live transmission, due to limited butter 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 usercentered quality metrics such stall rate and average bitrate. In our dataset, 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.