{
  "annotation_id": "2e0f917d-a77e-4983-9c34-8af66d8f8229",
  "date_created": "2026-02-17T05:53:24.329000Z",
  "date_modified": "2026-02-17T05:53:24.329000Z",
  "file_hash": "76c5ce630a3532b556f6818d2dbd486fe5bb1e714eb4ee2d99f6a841ca41596a",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.10607",
    "authors": [
      "Angeliki Katsenou",
      "Vignesh V. Menon",
      "Guoda Laurinaviciute",
      "Benjamin Bross",
      "Detlev Marpe"
    ],
    "categories": [
      "eess.IV",
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by-nc-sa/4.0/",
    "title": "Multi-Objective Pareto-Front Optimization for Efficient Adaptive VVC Streaming",
    "url": "https://arxiv.org/abs/2601.10607",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "0df675e4-9a45-4d2e-b0b3-084b489c29c1",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}