Q-learning based control algorithm for HTTP adaptive streaming

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.