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Energy-Quality-aware Variable Framerate Pareto-Front for Adaptive Video Streaming

Prajit T Rajendran, Samira Afzal, Vignesh V Menon, Christian Timmerer

TL;DR

Decoding-complexity aware Framerate Prediction (DECODRA) is proposed, which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints, and extends mobile device battery life and reduces the energy footprint of streaming services by providing a more energy-efficient video streaming pipeline.

Abstract

Optimizing framerate for a given bitrate-spatial resolution pair in adaptive video streaming is essential to maintain perceptual quality while considering decoding complexity. Low framerates at low bitrates reduce compression artifacts and decrease decoding energy. We propose a novel method, Decoding-complexity aware Framerate Prediction (DECODRA), which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints. DECODRA dynamically adjusts the framerate based on current bitrate and spatial resolution, balancing trade-offs between framerate, perceptual quality, and decoding complexity. Extensive experimentation with the Inter-4K dataset demonstrates DECODRA's effectiveness, yielding an average decoding energy reduction of up to 13.45%, with minimal VMAF reduction of 0.33 points at a low-quality degradation threshold, compared to the default 60 fps encoding. Even at an aggressive threshold, DECODRA achieves significant energy savings of 13.45% while only reducing VMAF by 2.11 points. In this way, DECODRA extends mobile device battery life and reduces the energy footprint of streaming services by providing a more energy-efficient video streaming pipeline.

Energy-Quality-aware Variable Framerate Pareto-Front for Adaptive Video Streaming

TL;DR

Decoding-complexity aware Framerate Prediction (DECODRA) is proposed, which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints, and extends mobile device battery life and reduces the energy footprint of streaming services by providing a more energy-efficient video streaming pipeline.

Abstract

Optimizing framerate for a given bitrate-spatial resolution pair in adaptive video streaming is essential to maintain perceptual quality while considering decoding complexity. Low framerates at low bitrates reduce compression artifacts and decrease decoding energy. We propose a novel method, Decoding-complexity aware Framerate Prediction (DECODRA), which employs a Variable Framerate Pareto-front approach to predict an optimized framerate that minimizes decoding energy under quality degradation constraints. DECODRA dynamically adjusts the framerate based on current bitrate and spatial resolution, balancing trade-offs between framerate, perceptual quality, and decoding complexity. Extensive experimentation with the Inter-4K dataset demonstrates DECODRA's effectiveness, yielding an average decoding energy reduction of up to 13.45%, with minimal VMAF reduction of 0.33 points at a low-quality degradation threshold, compared to the default 60 fps encoding. Even at an aggressive threshold, DECODRA achieves significant energy savings of 13.45% while only reducing VMAF by 2.11 points. In this way, DECODRA extends mobile device battery life and reduces the energy footprint of streaming services by providing a more energy-efficient video streaming pipeline.
Paper Structure (18 sections, 2 equations, 4 figures, 2 tables, 1 algorithm)

This paper contains 18 sections, 2 equations, 4 figures, 2 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Framerate Optimization in Adaptive Video Streaming
  3. Decoding Complexity-Aware Framerate Optimization (DECODRA)
  4. Optimization problem formulation
  5. Implementation
  6. Spatial downsampling
  7. Temporal downsampling
  8. Encoding
  9. Temporal upsampling
  10. Spatial upsampling
  11. Decoding
  12. Quality and energy measurement
  13. Pareto-front estimation
  14. Evaluation setup
  15. State-of-the-art
  16. ...and 3 more sections

Figures (4)

  • Figure 1: Rate-VMAF and rate-decoding energy for multiple framerates for Sequence 0286 of Inter-4K dataset inter4k_ref encoded using x265 encoder at veryslow preset for Apple HLS ladder HLS_ladder_ref.
  • Figure 2: DECODRA implementation.
  • Figure 3: Framerate decision, rate-VMAF, and rate-decoding energy curves of representative sequences of Inter-4K dataset for the Default, HQ, and DECODRA.
  • Figure 4: Average VMAF decrease and decoding energy reduction observed for each representation compared to HQ for the Inter-4K dataset.