Towards Optimal Multirate Encoding for HTTP Adaptive Streaming

Abstract

HTTP Adaptive Streaming (HAS) enables high quality streaming of video contents. In HAS, videos are divided into short intervals called segments, and each segment is encoded at various quality/bitrates to adapt to the available channel rate. Multiple encoding of the same content imposes high cost for video content providers. To reduce the time-complexity of encoding multiple representations, state-of-the-art methods typically encode the highest quality representation first and reuse the information gathered during its encoding to accelerate the encoding of the remaining representations. As encoding the highest quality representation requires the highest time-complexity compared to the lower quality representations, it would be a bottleneck in parallel encoding scenarios and the overall time-complexity will be limited to the time-complexity of the highest quality representation. In this paper and to address this problem, we consider all representations from the highest to the lowest quality representation as a potential, single reference to accelerate the encoding of the other, dependent representations. We formulate a set of encoding modes and assess their performance in terms of BD-Rate and time-complexity, using both VMAF and PSNR as objective metrics. Experimental results show that encoding a middle quality representation as a reference, can significantly reduce the maximum encoding complexity and hence it is an efficient way of encoding multiple representations in parallel. Based on this fact, a fast multirate encoding method is proposed which utilizes depth and prediction mode of a middle quality representation to accelerate the encoding of the dependent representations.

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