# Multi-Objective Pareto-Front Optimization for Efficient Adaptive VVC Streaming

arXiv: https://arxiv.org/abs/2601.10607 · HTML: https://mlsanity.com/paper/2601.10607/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 53 references that are in the map.
- First posted on arXiv 15 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [A Technical Overview of AV1](https://mlsanity.com/paper/2008.06091/) (2020)
- [Comyco: Quality-Aware Adaptive Video Streaming via Imitation Learning](https://mlsanity.com/paper/1908.02270/) (2019)
- [Efficient Bitrate Ladder Construction for Content-Optimized Adaptive Video Streaming](https://mlsanity.com/paper/2102.04550/) (2021)
- [Green Video Complexity Analysis for Efficient Encoding in Adaptive Video Streaming](https://mlsanity.com/paper/2304.12384/) (2023)
- [VMAF-based Bitrate Ladder Estimation for Adaptive Streaming](https://mlsanity.com/paper/2103.07564/) (2021)
- [Ensemble Learning for Efficient VVC Bitrate Ladder Prediction](https://mlsanity.com/paper/2207.10317/) (2022)
- [Decoding-Energy-Rate-Distortion Optimization for Video Coding](https://mlsanity.com/paper/2203.01099/) (2022)
- [Bitrate Ladder Construction using Visual Information Fidelity](https://mlsanity.com/paper/2312.07780/) (2023)
- [Towards AI-Assisted Sustainable Adaptive Video Streaming Systems: Tutorial and Survey](https://mlsanity.com/paper/2406.02302/) (2024)
- [On the benefit of parameter-driven approaches for the modeling and the prediction of Satisfied User Ratio for compressed video](https://mlsanity.com/paper/2206.09854/) (2022)
- [Convex Hull Prediction Methods for Bitrate Ladder Construction: Design, Evaluation, and Comparison](https://mlsanity.com/paper/2310.15163/) (2023)
- [Estimating the HEVC Decoding Energy Using the Decoder Processing Time](https://mlsanity.com/paper/2203.01767/) (2022)
- [Optimized Decoding-Energy-Aware Encoding in Practical VVC Implementations](https://mlsanity.com/paper/2206.13483/) (2022)

## Built on by (most cited first, 1 of 1)

- [Fast Multirate Encoding for 360deg Video in OMAF Streaming Workflows](https://mlsanity.com/paper/2601.17568/) (2026)

## Abstract (arXiv)

Adaptive video streaming has facilitated improved video streaming over the past years. A balance among coding performance objectives such as bitrate, video quality, and decoding complexity is required to achieve efficient, content- and codec-dependent, adaptive video streaming. This paper proposes a multi-objective Pareto-front (PF) optimization framework to construct quality-monotonic, content-adaptive bitrate ladders Versatile Video Coding (VVC) streaming that jointly optimize video quality, bitrate, and decoding time, which is used as a practical proxy for decoding energy. Two strategies are introduced: the Joint Rate-Quality-Time Pareto Front (JRQT-PF) and the Joint Quality-Time Pareto Front (JQT-PF), each exploring different tradeoff formulations and objective prioritizations. The ladders are constructed under quality monotonicity constraints during adaptive streaming to ensure a consistent Quality of Experience (QoE). Experiments are conducted on a large-scale UHD dataset (Inter-4K), with quality assessed using PSNR, VMAF, and XPSNR, and complexity measured via decoding time and energy consumption. The JQT-PF method achieves 11.76% average bitrate savings while reducing average decoding time by 0.29% to maintain the same XPSNR, compared to a widely-used fixed ladder. More aggressive configurations yield up to 27.88% bitrate savings at the cost of increased complexity. The JRQT-PF strategy, on the other hand, offers more controlled tradeoffs, achieving 6.38 % bitrate savings and 6.17 % decoding time reduction. This framework outperforms existing methods, including fixed ladders, VMAF- and XPSNR-based dynamic resolution selection, and complexity-aware benchmarks. The results confirm that PF optimization with decoding time constraints enables sustainable, high-quality streaming tailored to network and device capabilities.