SEED: Energy and Emission Estimation Dataset for Adaptive Video Streaming
Samira Afzal; Narges Mehran; Farzad Tashtarian; Radu Prodan; Christian Timmerer (2026): SEED: Energy and Emission Estimation Dataset for Adaptive Video Streaming In: 2025 International Conference on Visual Communications and Image Processing (VCIP).
The environmental impact of video streaming is gaining more attention due to its growing share in global internet traffic and energy consumption. To support accurate and transparent sustainability assessments, we present SEED (Streaming Energy and Emission Dataset): an open dataset for estimating energy usage and CO2 emissions in adaptive video streaming. SEED comprises 500 video segments. It provides segment-level measurements of energy consumption and emissions for two primary stages: provisioning, which encompasses encoding and storage on cloud infrastructure; and end-user consumption, including network interface retrieval, video decoding, and display on end-user devices. The dataset covers multiple codecs (AVC, HEVC), resolutions, bitrates, cloud instance types, and geographic regions, reflecting real-world variations in computing efficiency and regional carbon intensity. By combining empirical benchmarks with component-level energy models, SEED enables detailed analysis and supports the development of energy- and emission-aware adaptive bitrate (ABR) algorithms. The dataset is publicly available at: https://github.com/cd-athena/SEED.
