No abstract

Adaptive HTTP streaming requires a video to be encoded at different rates and qualities called representations. The encoding of multiple representations with the new video coding standard HEVC is computationally complex. In this paper, we propose a multi-rate encoding method which reduces the complexity of encoding a video at multiple spatial resolutions. We first examine block structure similarities at different resolutions and propose a method to derive the block structure for a low resolution representation from a reference high resolution encoding. The derived block structure is used to speed up the encoding of low resolution representations. We further consider the content of the videos in order to achieve a rate-distortion (RD) performance similar to independent HEVC encoding. Experimental results show that the encoding time can be reduced by 50% on average for a low resolution video without degrading the RD performance.

HTTP adaptive streaming of video content becomes an integrated part of the Internet and dominates other streaming protocols and solutions. The duration of creating video content for adaptive streaming ranges from seconds or up to several hours or days, due to the plethora of video transcoding parameters and video source types. Although, the computing resources of different transcoding platforms and services constantly increase, accurate and fast transcoding time prediction and scheduling is still crucial. We propose in this paper a novel method called fast video transcoding time prediction and scheduling (FastTTPS) of x264 encoded videos based on three phases: (i) transcoding data engineering, (ii) transcoding time prediction, and (iii) transcoding scheduling. The first phase is responsible for video sequence selection, segmentation and feature data collection required for predicting the transcoding time. The second phase develops an artificial neural network (ANN) model for segment transcoding time prediction based on transcoding parameters and derived video complexity features. The third phase compares a number of parallel schedulers to map the predicted transcoding segments on the underlying high-performance computing resources. Experimental results show that our predictive ANN model minimizes the transcoding mean absolute error (MAE) and mean square error (MSE) by up to 1.7 and 26.8, respectively. In terms of scheduling, our method reduces the transcoding time by up to 38% using a Max–Min algorithm compared to the actual transcoding time without prediction information.