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HTTP Adaptive Streaming (HAS) is becoming a key technology for audiovisual broadcasting, although the varying conditions of the networks imply an uneven video quality. So, there are currently many client players endowed with proprietary adaptation algorithms aiming at maximizing the perceived quality. Looking for the best user’s Quality of Experience (QoE), an adaptation algorithm based on the Q-Learning...
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.
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...
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