User Acceptance Model for Smart Incentives in Sustainable Video Streaming towards 6G

TL;DR

This paper introduces a user-acceptance model for sustainable video streaming incentives that balances energy efficiency with user perception, using personalized, adaptive, and data-driven strategies to promote environmentally friendly behavior in 6G networks.

Contribution

It develops a novel probabilistic framework integrating environmental awareness, personalization, and altruism, with strategies for education and data-driven user trait learning to enhance sustainability in video streaming.

Findings

Personalized incentives improve user acceptance of lower bitrates.

Gradual behavioral adaptation balances provider costs and network flexibility.

Incentive strategies can meet sustainability goals without harming user experience.

Abstract

The rapid growth of 5G video streaming is intensifying energy consumption across access, core, and data-center networks, underscoring the critical need for energy and carbon-efficient solutions. While reducing streaming bitrates improves energy efficiency, its success hinges on user acceptance--particularly when lower bitrates may be perceived as reduced quality of experience (QoE). Therefore, there is a need to develop transparent, user-centric incentive models that balance sustainability with perceived value. We propose a user-acceptance model that combines diverse environmental awareness, personalized responsiveness to incentives, and varying levels of altruism into a unified probabilistic framework. The model incorporates dynamic, individualized incentives that adapt over time. We further enhance the framework by incorporating (i) social well-being as a motivator for altruistic choices, (ii) provider-driven education strategies that gradually adjust user acceptance thresholds, and (iii) data-driven learning of user traits from historical offer--response interactions. Extensive synthetic-data experiments reveal the trade-offs between provider cost and network flexibility, showing that personalized incentives and gradual behavioral adaptation can advance sustainability targets without compromising stakeholder requirements.