Incentivising green video streaming through a 2-tier subscription model with carbon-aware rewards
2026-04-09 • Networking and Internet Architecture
Networking and Internet Architecture
AI summaryⓘ
The authors study ways to encourage users to reduce carbon emissions from video streaming by adjusting video quality. They propose a two-level subscription plan offering discounts and carbon rewards based on how often videos are streamed at lower quality. Their approach considers different types of users and the carbon intensity of data centers, allowing providers to limit quality reduction to a set percentage of videos. They find that the best strategy is to lower video quality by just one resolution step to balance user satisfaction and emissions. Additionally, the incentives vary depending on whether videos come from local or remote data centers with different environmental impacts.
carbon emissionsvideo streamingsubscription modelcarbon intensityenergy consumptiondata centersvideo qualityuser incentivesenvironmental consciousness
Authors
Vasilios A. Siris, Adamantia Stamou, George D. Stamoulis, Konstantinos Varsos
Abstract
We investigate incentives for reducing the carbon emissions of video streaming that depend on the energy consumption of segments in the end-to-end video delivery path, the carbon intensity, and the user type, i.e., quality-sensitive and green or environmentally conscious users. The incentives can be offered through a practical 2-tier subscription model with a discount and carbon rewards, which gives providers the flexibility to reduce the quality for up to a maximum percentage of videos within a time period, such as one month. The key features of our approach are i) it is preferable to offer subscriptions where the reduced-quality tier is set one resolution level below the resolution required for maximum user satisfaction; ii) when a video is streamed from a local data center, the maximum percentage of videos streamed at a lower quality depends solely on the carbon intensity and the average intensity cap, whereas the incentives also depend on the users' level of environmental consciousness; iii) when a video can be streamed from a local or a remote data center with different carbon intensities, the maximum percentage of videos streamed at lower quality and the incentives depend on the relative carbon intensity and energy consumption at the data centers, and the additional network energy costs from the remote data center.