Optimal Energy-Aware Service Management in Future Networks with a Gamified Incentives Mechanism

TL;DR

The paper tackles rising energy demand in future networks by proposing an energy-aware service-management framework that couples greenness-sensitive QoE with gamified incentives. A stochastic user-acceptance model is used, with each user characterized by a greenness factor $\gamma_n$ and a private threshold $r_{\min,n}$, and acceptance driven by a sigmoid $p_n(r_n)$. A serious-game component based on top-$K$ and bottom-$M$ rankings provides social motivation, while a Stackelberg optimization governs the provider's choice of $(K,M)$ and incentives under a budget. Empirical results on synthetic data show substantial traffic and energy reductions, with up to $67.2\%$ traffic reduction in realistic scenarios, especially when $K$ is large and $M$ is small and the social reward $H$ is high. The work offers a practical pathway for operators to steer user behavior toward greener streaming with measurable QoE preservation and energy savings.

Abstract

As energy demands surge across ICT infrastructures, service providers must engage users in sustainable practices while maintaining the Quality of Experience (QoE) at acceptable levels. In this paper, we introduce such an approach, leveraging gamified incentives and a model for user's acceptance on incentives, thus encouraging energy-efficient behaviors such as adaptive bitrate streaming. Each user is characterized by an environmental sensitivity factor and a private incentive threshold, shaping probabilistic responses to energy-saving offers. A serious-game mechanism based on positive behavioral reinforcement and rewards of the users, due to their inclusion in top-K and bottom-M rankings, fosters peer comparison and competition, thus transforming passive acceptance into active engagement. Moreover, within a Stackelberg game formulation, the video streaming service provider--acting as the strategic leader--optimizes both incentive levels and game parameters to achieve network-wide energy and traffic reductions, while adhering to budgetary constraints. This structured approach empowers providers with proactive, application-level control over energy consumption, offering them measurable benefits such as reduced high-bitrate traffic and increased participation in energy-saving behaviors, while also considering user satisfaction. The results of our simulations show that indeed gamification boosts significantly user participation and energy savings provided that the incentive and game parameters are chosen optimally.

Figures (11)

• Figure 1: Percentage of traffic reduction with $r_n \sim \mathcal{N}(1,0.25)$ and $H = 1000$.
• Figure 2: Percentage of traffic reduction for $r_n \sim \mathcal{N}(1, 0.25)$ and $H = 1$.
• Figure 3: Percentage of traffic reduction with $r_n \sim \mathcal{N}(3, 4)$ and $H = 1000$.
• Figure 4: Percentage of traffic reduction for $r_n \sim \mathcal{N}(10, 16)$ and $H = 1000$.
• Figure 5: Percentage of traffic reduction with $r_n \sim \mathcal{LN}(\nu, \rho^2)$, when $\mu = 1$ and $\sigma = 0.5$, and $H = 1000$.