# Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming

arXiv: https://arxiv.org/abs/2102.04550 · HTML: https://mlsanity.com/paper/2102.04550/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Feb 2021.

## Built on by (most cited first, 9 of 9)

- [VMAF-based Bitrate Ladder Estimation for Adaptive Streaming](https://mlsanity.com/paper/2103.07564/) (2021)
- [Convex Hull Prediction for Adaptive Video Streaming by Recurrent Learning](https://mlsanity.com/paper/2206.04877/) (2022)
- [Quality-Constant Per-Shot Encoding by Two-Pass Learning-based Rate Factor Prediction](https://mlsanity.com/paper/2208.10739/) (2022)
- [Multi-Objective Pareto-Front Optimization for Efficient Adaptive VVC Streaming](https://mlsanity.com/paper/2601.10607/) (2026)
- [Comparison of HDR quality metrics in Per-Clip Lagrangian multiplier optimisation with AV1](https://mlsanity.com/paper/2303.16163/) (2023)
- [Efficient Bitrate Ladder Construction using Transfer Learning and Spatio-Temporal Features](https://mlsanity.com/paper/2401.03195/) (2024)
- [Optimal Transcoding Resolution Prediction for Efficient Per-Title Bitrate Ladder Estimation](https://mlsanity.com/paper/2401.04405/) (2024)
- [Content Adaptive Encoding For Interactive Game Streaming](https://mlsanity.com/paper/2511.22327/) (2025)
- [A Self-Supervised Learning Framework for Video Encoding Complexity Clustering](https://mlsanity.com/paper/2606.29166/) (2026)

## Abstract (arXiv)

One of the challenges faced by many video providers is the heterogeneity of network specifications, user requirements, and content compression performance. The universal solution of a fixed bitrate ladder is inadequate in ensuring a high quality of user experience without re-buffering or introducing annoying compression artifacts. However, a content-tailored solution, based on extensively encoding across all resolutions and over a wide quality range is highly expensive in terms of computational, financial, and energy costs. Inspired by this, we propose an approach that exploits machine learning to predict a content-optimized bitrate ladder. The method extracts spatio-temporal features from the uncompressed content, trains machine-learning models to predict the Pareto front parameters, and, based on that, builds the ladder within a defined bitrate range. The method has the benefit of significantly reducing the number of encodes required per sequence. The presented results, based on 100 HEVC-encoded sequences, demonstrate a reduction in the number of encodes required when compared to an exhaustive search and an interpolation-based method, by 89.06% and 61.46%, respectively, at the cost of an average Bjontegaard Delta Rate difference of 1.78% compared to the exhaustive approach. Finally, a hybrid method is introduced that selects either the proposed or the interpolation-based method depending on the sequence features. This results in an overall 83.83% reduction of required encodings at the cost of an average Bjontegaard Delta Rate difference of 1.26%.