On Optimizing Adaptive Algorithms Based on Rebuffering Probability

Traditionally, video adaptive algorithms aim to select the representation that better fits to the current download rate. In recent years, a number of new approaches appeared that take into account the buffer occupancy and the probability of video rebuffering as important indicators of the representation to be selected. We propose an optimization of the existing algorithm based on rebuffering probability and argue that the algorithm should avoid the situations when the client buffer is full and the download is stopped, since these situations decrease the efficiency of the algorithm. Reducing full buffer states does not increase the rebuffering probability thanks to a clever management of the client buffer, which analyses the buffer occupancy and downloads higher bitrate representations only in the case of high buffer occupancy.

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