Paper accepted @ Multimedia Tools and Applications

Title: Complexity prediction of hardware and software video transcoding in the cloud

Authors: Taieb Chachou, Sid Ahmed Fezza, Wassim Hamidouche, Ghalem Belalem, Hadi Amirpour

Abstract: Today, video content constitutes a significant portion of internet traffic. This video can be viewed by a wide range of devices with varying characteristics and under different network conditions. Video transcoding is a crucial mechanism for adapting video content to this diverse array of devices and bandwidth requirements while ensuring the best possible user experience. However, video transcoding is a computationally intensive process, requiring scalable infrastructure like cloud computing to efficiently handle the complexity and volume of tasks. In this paper, we propose a novel method to predict transcoding time across different types of platforms (CPU and GPU) and codecs (H.264/AVC, H.265/HEVC).

Unlike existing approaches that focus mainly on CPU-based transcoding, the proposed model explicitly considers hardware-accelerated (GPU) transcoding, where accelerators significantly influence video transcoding performance in cloud computing.

The predicted transcoding time can be utilized to optimize the scheduling of transcoding tasks in cloud computing, helping to ensure optimal load balancing and minimize total transcoding time while maintaining the highest video quality. The proposed solution consists of two essential phases: (i) dataset construction and (ii) model construction. The first phase involves video selection, segmentation, and video transcoding. The second phase focuses on analyzing the most important features that influence the prediction of transcoding time and developing a machine learning-based model for accurate video transcoding time prediction. Experimental results demonstrate that the XGBoost model achieves superior prediction accuracy across both software and hardware codecs, achieving a global coefficient of determination of R²~=~0.993 when evaluated on the complete dataset, which includes video segments transcoded using H.264/AVC and H.265/HEVC codecs on CPU and GPU platforms. This performance represents an improvement of approximately 7.45% compared to state-of-the-art methods.