Abstract
Video streaming now represents the dominant share of Internet traffic, as ever-higher-resolution content is distributed across a growing range of heterogeneous devices to sustain user Quality of Experience (QoE). However, this trend raises significant concerns about energy efficiency and carbon emissions, requiring methods to provide a trade-off between energy and QoE. This paper proposes a lightweight energy prediction method that estimates the energy consumption of high-resolution video encodings using reference encodings generated at lower resolutions (so-called anchors), eliminating the need for exhaustive per-segment energy measurements, a process that is infeasible at scale. We automatically select encoding parameters, such as resolution and quantization parameter (QP), to achieve substantial energy savings while maintaining perceptual quality, as measured by the Video Multimethod Fusion Assessment (VMAF), within acceptable limits. We implement and evaluate our approach with the open-source VVenC encoder on 100 video sequences from the Inter4K dataset across multiple encoding settings. Results show that, for an average VMAF score reduction of only 1.68, which stays below the Just Noticeable Difference (JND) threshold, our method achieves 51.22 % encoding energy savings and 53.54 % decoding energy savings compared to a scenario with no quality degradation.
Keywords: Video Streaming, Video on Demand, Machine Learning, Energy Efficiency
How to Cite:
Azimi, Z., Farahani, R., Menon, V. & Timmerer, C., (2025) “Predicting Encoding Energy from Low-Pass Anchors for Green Video Streaming”, IoT Workshop Proceedings 1(1), 50-56. doi: https://doi.org/10.34749/3061-1008.2025.8
Rights: Copyright © 2025 The author(s)
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