[Paper] Multi-Objective Pareto-Front Optimization for Efficient Adaptive VVC Streaming

Source: arXiv - 2601.10607v1

Overview

The paper introduces a multi‑objective Pareto‑front optimization framework for building adaptive bitrate ladders in Versatile Video Coding (VVC) streams. By jointly considering video quality, bitrate, and decoding time (as a proxy for energy consumption), the authors demonstrate how to deliver higher‑quality video while cutting bandwidth and keeping device power usage in check.

Key Contributions

• Pareto‑front based ladder design: Two novel formulations—JRQT‑PF (joint rate‑quality‑time) and JQT‑PF (joint quality‑time)—that generate content‑aware, quality‑monotonic bitrate ladders.
• Quality‑monotonicity constraint: Guarantees that higher‑resolution or higher‑bitrate representations never produce lower perceived quality, preserving a smooth Quality of Experience (QoE).
• Comprehensive evaluation on a large‑scale 4K UHD dataset (Inter‑4K) using PSNR, VMAF, and XPSNR for quality, and decoding time/energy for complexity.
• Significant savings: JQT‑PF achieves up to 27.9 % bitrate reduction with minimal decoding‑time impact; JRQT‑PF delivers balanced gains of ≈6 % bitrate and ≈6 % decoding‑time reductions.
• Benchmark superiority: Outperforms fixed ladders, VMAF‑based dynamic resolution selection, and other complexity‑aware baselines.

Methodology

1. Data preparation – The authors encode each source video at multiple resolutions, bitrates, and VVC configurations, measuring resulting quality scores (PSNR, VMAF, XPSNR) and decoding time on a reference device.
2. Pareto‑front construction –
   • JRQT‑PF treats bitrate, quality, and decoding time as three simultaneous objectives.
   • JQT‑PF fixes bitrate (or treats it as a secondary constraint) and optimizes only quality vs. decoding time.
   • For each content item, the non‑dominated points (i.e., no other point is better in all objectives) form the Pareto front.
3. Ladder extraction – From the Pareto front, a monotonic ladder is selected by enforcing that moving up the ladder never decreases quality. This yields a set of “profiles” that an adaptive streaming client can switch between.
4. Evaluation pipeline – Simulated adaptive streaming sessions compare the proposed ladders against traditional fixed ladders and other dynamic schemes, measuring average bitrate, decoding time, and energy consumption while keeping the target quality constant.

Results & Findings

• Quality preservation: All methods maintain the same XPSNR (or VMAF) as the baseline, confirming that the bitrate savings do not degrade perceived quality.
• Trade‑off flexibility: JQT‑PF is aggressive on bitrate at the cost of a modest increase in decoding time, suitable for high‑bandwidth networks with powerful clients. JRQT‑PF offers a more balanced reduction in both bitrate and processing load, ideal for constrained devices.
• Robustness across content: The Pareto‑front approach automatically adapts to scene complexity, motion, and texture, delivering content‑specific ladders without manual tuning.

Practical Implications

• Streaming services can integrate the Pareto‑front ladder generator into their encoding pipelines to produce dynamic, device‑aware playlists that reduce CDN bandwidth bills while keeping QoE stable.
• Edge and mobile platforms benefit from the decoding‑time/energy reductions, extending battery life and lowering thermal throttling on smartphones, tablets, and set‑top boxes.
• Adaptive bitrate (ABR) algorithms can be enhanced to query the Pareto‑front ladder for the “best‑fit” representation given current network bandwidth and device capability, rather than relying on static, one‑size‑fits‑all ladders.
• VVC adoption becomes more attractive: the framework mitigates one of the main concerns—higher decoder complexity—by explicitly accounting for it during ladder construction.
• Open‑source tooling: The authors’ methodology can be packaged as a command‑line utility or library (e.g., Python + FFmpeg bindings) that takes a set of encoded assets and outputs a JSON ladder ready for DASH/HLS manifests.

Limitations & Future Work

• Decoder‑specific measurements: Decoding time and energy were measured on a single hardware configuration; results may vary across GPUs, ARM CPUs, or specialized ASIC decoders.
• Static content analysis: The Pareto front is built offline per video; real‑time content changes (e.g., live streaming) would require on‑the‑fly estimation or predictive models.
• Objective weighting: The current formulations treat objectives equally or with simple constraints; more sophisticated utility functions (e.g., user‑centric QoE models) could further refine trade‑offs.
• Scalability to massive catalogs: Generating Pareto fronts for thousands of titles could be computationally intensive; future work may explore machine‑learning surrogates to approximate the front quickly.

Overall, the paper provides a solid, engineering‑focused pathway to smarter VVC streaming that balances bandwidth, visual fidelity, and device power—key concerns for any modern video platform.

Authors

• Angeliki Katsenou
• Vignesh V. Menon
• Guoda Laurinaviciute
• Benjamin Bross
• Detlev Marpe

Paper Information

• arXiv ID: 2601.10607v1
• Categories: eess.IV, cs.CV
• Published: January 15, 2026
• PDF: Download PDF