Content providers create different versions of a video to accommodate different end-user devices and network conditions. However, each of these versions requires a resource intensive encoding process. To reduce the computational complexity of the encodings, this paper proposes a fast simultaneous encoder. This encoder takes a single video as input and creates a number of bit streams encoded with different parameters. Only one version of the video is created with a full encode, whereas encoding of the other versions is accelerated by exploiting the correlation with the fully encoded version using machine learning techniques. In a practical scenario, the fast simultaneous encoder achieves a complexity reduction of 67.3% with a bit rate increase of 5.2% compared to performing a full encode of each version.
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