A Survey on Energy Consumption and Environmental Impact of Video Streaming

TL;DR

This survey addresses the environmental impact and energy consumption of video streaming by examining both provisioning (encoding, storage, and delivery) and consumption (retrieval, decoding, and display) components. It employs a systematic literature review to build a taxonomy of energy-influencing factors, synthesizes state-of-the-art methods (including AI-based encoding and energy-aware decoding), and catalogs relevant datasets and tools. Key contributions include identifying gaps such as fixed bitrate ladders in HLS/DASH, inefficient video players, and a lack of reproducible energy measurement datasets across devices and codecs. The study's findings highlight the uneven carbon intensity of electricity grids, the importance of cross-layer optimization, and the potential for edge/cloud strategies and green ABR to meaningfully reduce emissions, offering practical guidance for researchers, service providers, and policymakers.

Abstract

Climate change challenges require a notable decrease in worldwide greenhouse gas (GHG) emissions across technology sectors. Digital technologies, especially video streaming, accounting for most Internet traffic, make no exception. Video streaming demand increases with remote working, multimedia communication services (e.g., WhatsApp, Skype), video streaming content (e.g., YouTube, Netflix), video resolution (4K/8K, 50 fps/60 fps), and multi-view video, making energy consumption and environmental footprint critical. This survey contributes to a better understanding of sustainable and efficient video streaming technologies by providing insights into the state-of-the-art and potential future directions for researchers, developers, and engineers, service providers, hosting platforms, and consumers. We widen this survey's focus on content provisioning and content consumption based on the observation that continuously active network equipment underneath video streaming consumes substantial energy independent of the transmitted data type. We propose a taxonomy of factors that affect the energy consumption in video streaming, such as encoding schemes, resource requirements, storage, content retrieval, decoding, and display. We identify notable weaknesses in video streaming that require further research for improved energy efficiency: (1) fixed bitrate ladders in HTTP live streaming; (2) inefficient hardware utilization of existing video players; (3) lack of comprehensive open energy measurement dataset covering various device types and coding parameters for reproducible research.

Figures (6)

• Figure 1: Surveyed video streaming components from production to end-user consumption, comprising encoding, storage, retrieval, decoding, and display.
• Figure 2: Literature selection methodology inspired from turner2010does.
• Figure 3: (a) Categorization of articles based on scope, and (b) distribution in the past thirteen years.
• Figure 4: Energy consumption and environmental impact of video streaming survey structure.
• Figure 5: Distribution of codecs utilized in the related works for video encoding in Table \ref{['tbl:encoding']}.