Abstract
Today, video content is delivered to a myriad of devices over different communication networks. Video delivery must be adapted to the available bandwidth, screen size, resolution and the decoding capability of the end user devices. In this work we present an approach to predict the transcoding time of a video into another given transcoding parameters and an input video. To obtain enough information on the characteristics of real world online videos and their transcoding parameters needed to model transcoding time, we built a video characteristics dataset, using data collected from a large video-on-demand system, YouTube. The dataset contains a million randomly sampled video instances listing 10 fundamental video characteristics. We report our analysis on the dataset which provides insightful statistics on fundamental online video characteristics that can be further exploited to optimize or model components of a multimedia processing systems. We also present experimental results on transcoding time prediction models, based on support vector machines, linear regression and multi-layer perceptron feed forward artificial neural network.
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