Green Streaming

Context-based video encoding for an energy-efficient and sustainable streaming supply chain

May 01, 2023 to Apr. 30, 2026

Due to the continuous increase in media usage over the Internet through video streaming offers, energy efficiency and sustainability in the production, distribution, and playback of digital media along the entire value chain are becoming increasingly important. A more technically efficient video streaming provides the basis for a more energy-efficient and thus more sustainable streaming value chain.

The Green Streaming project considers all components along the streaming value chain in terms of their potential energy efficiency including A/V encoders, packaging, content delivery networks (CDN), A/V players and end devices. This requires innovative technologies and research work that define suitable measurement points and parameters, which build the foundation for required component-specific measurement data that can be collected and used to optimize the chain. Therefore, machine learning methods and AI (artificial intelligence) models can significantly improve, optimize, and support the subsequent processes and workflows.

Green digital twins of a holistically considered streaming value chain can model the digital representation of the physical processes and thus provide the basis for the data to be compiled for a more sustainable video streaming.

As a result, the project Green Streaming focusses on sustainable video technology for measurable and verifiable green streaming by conducting research work on technologies and systems for a measurable and thus assessable streaming value chain that can enable sustainable operation and use of streaming content in terms of its carbon footprint in an automated, adaptive and self-learning manner. The goal is to create innovations, concepts, and energy-efficient solutions addressing the entire video streaming value chain from video creation to processing, delivery, and their usage on consumer devices.

This image has no alt text.
Project overview Fraunhofer FOKUS