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

TL;DR

This paper proposes a 2-tier subscription model with carbon-aware rewards to incentivize greener video streaming, considering energy consumption, carbon intensity, and user preferences.

Contribution

It introduces a practical subscription framework that balances quality reduction and environmental incentives based on data center locations and user types.

Findings

Optimal quality reduction is set one resolution level below maximum satisfaction.

Incentives depend on local data center carbon intensity and user environmental consciousness.

Remote data center streaming affects incentives through relative carbon intensity and network energy costs.

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.