Sustainability in Video Encoding and Streaming:
Energy-Efficient Techniques and Metrics

Workshop on Media Energy Consumption Measurement and Exposure

[Workshop URL] [Slides] [PDF]

Presenter: Christian Timmerer (Alpen-Adria-Universität Klagenfurt

Abstract: The presentation discusses the increasing environmental impact of video streaming and highlights the urgent need for more sustainable approaches across the entire streaming pipeline. Video traffic dominates internet usage and contributes significantly to global greenhouse gas emissions, while the demand for higher quality content continues to drive up computational complexity and energy consumption in encoding, delivery, and playback.

A central insight is that there is a strong trade-off between video quality and energy consumption, where small reductions in quality can lead to substantial energy savings. By introducing energy as an explicit optimization objective, techniques such as content-aware encoding, energy-aware bitrate ladder construction, and real-time optimization for live streaming can significantly reduce energy usage while maintaining nearly the same perceptual quality.

The work also emphasizes the role of adaptive bitrate algorithms that incorporate energy consumption alongside traditional quality and buffer-based metrics. These approaches demonstrate that it is possible to simultaneously improve user experience and reduce energy consumption, indicating that sustainability and performance can be aligned rather than conflicting goals.

To enable such optimizations, the presentation introduces a range of metrics and models, including video complexity measures, quality prediction models, and machine learning-based approaches for estimating encoding and decoding energy as well as CO₂ emissions. These tools support more informed, data-driven decisions across the full streaming workflow from encoding to playback.

Another important theme is end-to-end optimization, where energy efficiency depends on the combined behavior of encoding strategies, bitrate selection, and client-side adaptation. Industry efforts confirm the practical relevance of these approaches and highlight the importance of collaboration and real-world validation.

Despite promising results, several challenges remain, including difficulties in measuring and benchmarking energy consumption, the lack of standardized methodologies, and the limited integration of energy considerations into existing workflows. Overall, the presentation argues that energy consumption should become a first-class optimization target in video streaming systems, similar to established quality metrics, to enable truly sustainable media delivery.

Keywords: sustainable streaming, energy-aware encoding, adaptive bitrate streaming, green multimedia, video compression, bitrate ladder optimization, QoE optimization, energy-quality tradeoff, video complexity analysis, CO2 footprint, energy modeling, machine learning for video, end-to-end optimization, eco-efficient streaming, real-time streaming optimization