The 15th ACM Multimedia Systems Conference (Open-source Software and Datasets)
15-18 April, 2024 | Bari, Italy
Farzad Tashtarian∗ (AAU, Austria), Daniele Lorenzi∗ (AAU, Austria), Hadi Amirpour (AAU, Austria), Samira Afzal (AAU, Austria), and Christian Timmerer (AAU, Austria)
*These authors contributed equally to this work
HTTP Adaptive Streaming (HAS) has emerged as the predominant solution for delivering video content on the Internet. The urgency of the climate crisis has accentuated the demand for investigations into the environmental impact of HAS techniques. In HAS, clients rely on adaptive bitrate (ABR) algorithms to drive the quality selection for video segments. Focusing on maximizing
video quality, these algorithms often prioritize maximizing video quality under favorable network conditions, disregarding the impact of energy consumption. To thoroughly investigate the effects
of energy consumption, including the impact of bitrate and other video parameters such as resolution and codec, further research is still needed. In this paper, we propose COCONUT, a COntent COnsumption eNergy measUrement daTaset for adaptive video streaming collected through a digital multimeter on various types of client devices, such as laptop and smartphone, streaming MPEG-DASH segments. Furthermore, we analyze the dataset and find insights into the influence of multiple codecs, various video encoding parameters, such as segment length, framerate, bitrates, and resolutions, and decoding type, i.e., hardware or software, on energy
consumption. We gather and categorize these measurements based on segment retrieval through the network interface card (NIC), decoding, and rendering. Additionally, we compare the impact of
different HAS players on energy consumption. This research offers valuable perspectives on the energy usage of streaming devices, which could contribute to creating a media consumption experience that is both more sustainable and resource-efficient.
