arXiv:2411.14613v1 Announce Type: new
Abstract: In today’s digital landscape, video content dominates internet traffic, underscoring the need for efficient video processing to support seamless live streaming experiences on platforms like YouTube Live, Twitch, and Facebook Live. This paper introduces a comprehensive framework designed to optimize video transcoding parameters, with a specific focus on preset and bitrate selection to minimize distortion while respecting constraints on bitrate and transcoding time. The framework comprises three main steps: feature extraction, prediction, and optimization. It leverages extracted features to predict transcoding time and rate-distortion, employing both supervised and unsupervised methods. By utilizing integer linear programming, it identifies the optimal sequence of presets and bitrates for video segments, ensuring real-time application feasibility under set constraints. The results demonstrate the framework’s effectiveness in enhancing video quality for live streaming, maintaining high standards of video delivery while managing computational resources efficiently. This optimization approach meets the evolving demands of video delivery by offering a solution for real-time transcoding optimization. Evaluation using the User Generated Content dataset showed an average PSNR improvement of 1.5 dB over the default Twitch configuration, highlighting significant PSNR gains. Additionally, subsequent experiments demonstrated a BD-rate reduction of -49.60%, reinforcing the framework’s superior performance over Twitch’s default configuration.
Optimizing Video Transcoding Parameters for Seamless Live Streaming
In today’s digital landscape, video content dominates internet traffic, and platforms like YouTube Live, Twitch, and Facebook Live have become popular for live streaming experiences. However, to ensure seamless live streaming, efficient video processing is crucial. This paper introduces a comprehensive framework that focuses on optimizing video transcoding parameters, specifically preset and bitrate selection, to minimize distortion while respecting constraints on bitrate and transcoding time.
The framework comprises three main steps: feature extraction, prediction, and optimization. By extracting relevant features, the framework predicts transcoding time and rate-distortion using both supervised and unsupervised methods. It then utilizes integer linear programming to identify the optimal sequence of presets and bitrates for video segments, ensuring real-time application feasibility under set constraints.
One impressive aspect of this framework is its multi-disciplinary nature. It combines concepts from various fields, including multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By incorporating knowledge from these diverse disciplines, the framework offers a holistic approach to optimizing video transcoding for live streaming.
The wider field of multimedia information systems contributes to the framework by providing techniques for feature extraction and prediction. These techniques enable the framework to analyze video content efficiently and make accurate predictions about transcoding time and rate-distortion. Additionally, the field of animations provides insights into rendering and encoding techniques, which can enhance the quality of live streaming videos.
Artificial reality, augmented reality, and virtual realities also play a role in this framework. These fields deal with immersive and interactive experiences, and their principles can be applied to live streaming to create a more engaging viewer experience. By optimizing transcoding parameters, the framework ensures that videos are delivered in real-time, supporting the seamless integration of augmented reality elements or virtual reality content during live streams.
The results obtained from the evaluation of the framework are highly promising. Using the User Generated Content dataset, the framework demonstrated an average PSNR improvement of 1.5 dB over the default Twitch configuration. This improvement highlights the significant gains in video quality achieved by the framework. Furthermore, subsequent experiments showed a BD-rate reduction of -49.60%, indicating the superior performance of the framework compared to Twitch’s default configuration.
In conclusion, this comprehensive framework presents a solution for real-time transcoding optimization in live streaming. With its multi-disciplinary approach and utilization of advanced techniques from multimedia information systems, animations, artificial reality, augmented reality, and virtual realities, it effectively enhances video quality while efficiently managing computational resources. As live streaming continues to grow in popularity, such optimization approaches will play a crucial role in delivering seamless, high-quality video experiences to viewers.