Paper accepted: Towards ML-Driven Video Encoding Parameter Selection for Quality and Energy Optimization

Authors: Zoha Azimi, Reza Farahani, Vignesh V Menon, Christian Timmerer, Radu Prodan

Venue: 16th International Conference on Quality of Multimedia Experience (QoMEX’24)

Abstract: As video streaming dominates Internet traffic, users constantly seek a better Quality of Experience (QoE), often resulting in increased energy consumption and a higher carbon footprint. The increasing focus on sustainability underscores the
critical need to balance energy consumption and QoE in video streaming. This paper proposes a modular architecture that refines video encoding parameters by assessing video complexity and encoding settings for the prediction of energy consumption and video quality (based on Video Multimethod Assessment Fusion (VMAF)) using lightweight XGBoost models trained on the multi-dimensional video compression dataset (MVCD). We apply Explainable AI (XAI) techniques to identify the critical encoding parameters that influence the energy consumption and video quality prediction models and then tune them using a weighting strategy between energy consumption and video quality. The experimental results confirm that applying a suitable weighting factor to energy consumption in the x265 encoder results in a 46 % decrease in energy consumption, with a 4-point drop in VMAF, staying below the Just Noticeable Difference (JND) threshold.