Cluster Computing paper: FastTTPS: Fast Approach for Video Transcoding Time Prediction and Scheduling for HTTP Adaptive Streaming Videos

Cluster Computing paper: FastTTPS: Fast Approach for Video Transcoding Time Prediction and Scheduling for HTTP Adaptive Streaming Videos

Cluster Computing (Springer Journal) [PDF]

Prateek Agrawal (University of Klagenfurt, Austria), Anatoliy Zabrovskiy (University of Klagenfurt, Austria), Adithyan Ilagovan (Bitmovin Inc., CA, USA), Christian Timmerer (University of Klagenfurt, Austria), Radu Prodan (University of Klagenfurt, Austria)

Abstract:

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.

Keywords: Transcoding time prediction, Video transcoding, Scheduling, Artificial neural networks, MPEG-DASH, Adaptive streaming

Acknowledgment: This work received support from Austrian Research Promotion Agency (FFG) under grant agreement 877503571 (APOLLO project) and European Union Horizon 2020 research and innovation programme under grant agreement 801091 (ASPIDE project).