Decoding Complexity-Rate-Quality Pareto-Front for Adaptive VVC Streaming

TL;DR

This paper develops a Pareto-front optimization framework for adaptive VVC streaming, enabling efficient trade-offs between bitrate, quality, and decoding complexity to improve streaming performance.

Contribution

It introduces a joint decoding time-rate-quality Pareto-front method for constructing bitrate ladders, enhancing the balance between multiple streaming objectives.

Findings

Achieves 14.86% decoding time reduction.

Provides 4.65% bitrate savings.

Improves quality by 0.32dB in XPSNR-Rate domain.

Abstract

Pareto-front optimization is crucial for addressing the multi-objective challenges in video streaming, enabling the identification of optimal trade-offs between conflicting goals such as bitrate, video quality, and decoding complexity. This paper explores the construction of efficient bitrate ladders for adaptive Versatile Video Coding (VVC) streaming, focusing on optimizing these trade-offs. We investigate various ladder construction methods based on Pareto-front optimization, including exhaustive Rate-Quality and fixed ladder approaches. We propose a joint decoding time-rate-quality Pareto-front, providing a comprehensive framework to balance bitrate, decoding time, and video quality in video streaming. This allows streaming services to tailor their encoding strategies to meet specific requirements, prioritizing low decoding latency, bandwidth efficiency, or a balanced approach, thus enhancing the overall user experience. The experimental results confirm and demonstrate these opportunities for navigating the decoding time-rate-quality space to support various use cases. For example, when prioritizing low decoding latency, the proposed method achieves decoding time reduction of 14.86% while providing Bjontegaard delta rate savings of 4.65% and 0.32dB improvement in the eXtended Peak Signal-to-Noise Ratio (XPSNR)-Rate domain over the traditional fixed ladder solution.