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.

翻译：在当今数字环境中，视频内容主导了互联网流量，这凸显了高效视频处理对于支持YouTube Live、Twitch和Facebook Live等平台上无缝直播体验的必要性。本文介绍了一个旨在优化视频转码参数的综合框架，特别关注预设和比特率的选择，以在满足比特率和转码时间约束的同时最小化失真。该框架包含三个主要步骤：特征提取、预测和优化。它利用提取的特征来预测转码时间和率失真性能，采用了有监督和无监督两种方法。通过运用整数线性规划，该框架为视频片段确定了最优的预设和比特率序列，确保了在设定约束下实时应用的可行性。结果表明，该框架在提升直播视频质量方面具有显著效果，在高效管理计算资源的同时保持了高标准的视频传输质量。这种优化方法通过提供实时转码优化解决方案，满足了视频传输不断发展的需求。使用用户生成内容数据集的评估显示，相较于Twitch的默认配置，平均PSNR提升了1.5 dB，突显了显著的PSNR增益。此外，后续实验表明BD-rate降低了-49.60%，进一步证实了该框架相对于Twitch默认配置的优越性能。