High-Quality Live Video Streaming via Transcoding Time Prediction and Preset Selection
Zahra Nabizadeh Shahre-Babak, Nader Karimi, Krishna Rapaka, Tarek Amara, Shadrokh Samavi, Shahram Shirani

TL;DR
This paper introduces a learning-based framework that predicts transcoding times for live video streaming presets, enabling optimal preset selection to improve quality and reduce delays with minimal overhead.
Contribution
The authors develop a minimal-delay, metadata-based prediction model for transcoding times across presets, enhancing live streaming quality and efficiency.
Findings
Prediction accuracy with 5% MAPE
Up to 5 dB PSNR improvement
Effective preset selection for live streaming
Abstract
Video streaming often requires transcoding content into different resolutions and bitrates to match the recipient's internet speed and screen capabilities. Video encoders like x264 offer various presets, each with different tradeoffs between transcoding time and rate-distortion performance. Choosing the best preset for video transcoding is difficult, especially for live streaming, as trying all the presets and choosing the best one is not feasible. One solution is to predict each preset's transcoding time and select the preset that ensures the highest quality while adhering to live streaming time constraints. Prediction of video transcoding time is also critical in minimizing streaming delays, deploying resource management algorithms, and load balancing. We propose a learning-based framework for predicting the transcoding time of videos across various presets. Our predictor's features…
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Taxonomy
TopicsImage and Video Quality Assessment · Video Coding and Compression Technologies · Video Analysis and Summarization
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
