🤖 AI Summary
This work addresses the challenge of adaptive video streaming by balancing bitrate, visual quality, and decoding complexity while accounting for content characteristics and ensuring consistent user experience—limitations not adequately met by existing approaches. The authors propose a multi-objective Pareto-front optimization framework that, for the first time, incorporates decoding time as a proxy for energy consumption. Within this framework, two strategies—JRQT-PF and JQT-PF—are introduced to generate content-adaptive, efficient bitrate ladders for VVC under a quality monotonicity constraint. Experimental results demonstrate that JQT-PF achieves an average bitrate saving of 11.76% with slightly reduced decoding time while maintaining XPSNR, whereas JRQT-PF yields 6.38% bitrate savings and a 6.17% reduction in decoding time, significantly outperforming both fixed-ladder and dynamic-resolution baselines.
📝 Abstract
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.