Energy Consumption and Carbon Emissions of Modern Software Video Encoders

In today's digital landscape, video streaming holds an important role in internet traffic, driven by the pervasive use of mobile devices and the surge in streaming platform popularity. In light of this, the imperative to gauge energy consumption takes center stage, paving the way for eco-conscious and sustainable video streaming solutions with a minimal Carbon Dioxide (CO2) footprint. This paper meticulously examines the energy consumption and CO2 emissions of five popular open-source and fast video encoders: x264, x265, VVenC, libvpx-vp9, and SVT-AV1. These encoders are optimized software implementations of three video coding standards (AVC/H.264, HEVC/H.265, VVC/H.266) and two video formats (VP9andAV1).Toensureafaircomparison,wealsoassesscodingefficiencyacross these encoders at four distinct presets, applying three objective quality metrics. Additional factors like computing density and memory usage are considered. Our experiments employ the JVET-CTC video dataset, encompassing video sequences of diverse content and resolutions. Encoding is executed on an Intel x86 multi-core processor, while CO2 emissions are computed based on the energy mix data from a server situated in France, reflecting an average emission rate of 101 g/kWh. Our findings underscore that the x264 and SVT-AV1 encoders, especially at fast and faster presets, exhibit the lowest energy consumption and CO2 emissions. Notably, x264 boasts the most energy-efficient performance, yielding CO2 emissions of 0.28 g, 0.91 g, 2.07 g, and 9.74 g when encoding videos using faster, fast, medium, and slower presets, respectively. Furthermore, SVT-AV1 and VVenC encoders operating at a slower preset demonstrate superior coding efficiency, albeit at the cost of higher computational complexity and CO2 emissions of 60.5 g and 406 g, respectively. A salient observation from our study is that resolution and encoder presets serve as crucial parameters for curbing energy consumption and CO2 emissions, albeit with an inherent trade-off in video quality. Comprehensive results from this research are publicly accessible
https://chachoutaieb.github.io/encoding
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