Optimal Transcoding Preset Selection for Live Video Streaming
Zahra Nabizadeh, Maedeh Jamali, Nader Karimi, Shadrokh Samavi, Shahram, Shirani

TL;DR
This paper presents a framework for optimizing live video transcoding parameters, focusing on preset and bitrate selection to improve video quality and efficiency in real-time streaming platforms.
Contribution
It introduces a novel optimization framework combining feature prediction and integer linear programming for real-time transcoding parameter selection.
Findings
Average PSNR improvement of 1.5 dB over default configurations
BD-rate reduction of -49.60% demonstrating superior performance
Effective real-time application with computational resource management
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…
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Taxonomy
TopicsAdvanced Data Compression Techniques · Video Coding and Compression Technologies · Image and Video Quality Assessment
