No abstract

Abstract-We propose a Q-Learning-based algorithm for an HTTP Adaptive Streaming (HAS) Client that maximizes the perceived quality, taking into account the relation between the estimated bandwidth and the qualities and penalizing the freezes. The results will show that it produces an optimal control as other approaches do, but keeping the adaptiveness.

No abstract

No abstract

Abstract-We present a control algorithm based on Q-Learning for an HTTP Adaptive Streaming (HAS) Client in order to optimize the Quality of Experience (QoE) of the user. First, we propose a model with a suitable number of variables in an attempt to find a reasonable tradeoff between the complexity of the model and its capacity to capture appropriately the dynamics of the system. Second, we define a novel reward function that takes into consideration factors related to the user's QoE. Results will show, that our Q-learning algorithm is able to learn and efficiently control the selection of the segment qualities. In addition, we will show that our proposed approach outperforms another Q-learning approach.