Incentivising green video streaming through a 2-tier subscription model with carbon-aware rewards

2026-04-09

AI Summary

This work proposes a two-tier subscription incentive mechanism that integrates discounts and carbon credit rewards to reconcile heterogeneous user preferences for video quality and environmental sustainability in streaming services. By strategically allowing modest reductions in video resolution for select users, the approach reduces carbon emissions while preserving quality of experience. The framework innovatively unifies user heterogeneity, spatial variations in carbon intensity between local and remote data centers, and network energy consumption into a single optimization model. It establishes that the optimal resolution downgrade corresponds to the next lower tier that still satisfies user satisfaction thresholds. Coupled with a carbon-aware scheduling algorithm and a dynamic pricing model, the system achieves substantial carbon footprint reductions, particularly in scenarios with environmentally conscious users and green-powered data centers.

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.

Problem

• green video streaming
• carbon emissions
• incentive mechanism
• user type
• carbon intensity

Innovation

• carbon-aware streaming
• two-tier subscription
• green incentives
• video quality adaptation
• data center carbon intensity