Energy-Efficient Caching for Scalable Videos in Heterogeneous Networks

TL;DR

This paper introduces two novel energy-efficient caching schemes for 5G heterogeneous networks using scalable video coding, aiming to improve energy efficiency and service quality for on-demand video streaming.

Contribution

The paper proposes SVC-based fractional and random caching schemes, deriving performance metrics and formulating EE maximization problems, solved efficiently with gradient projection methods.

Findings

Proposed schemes outperform benchmark strategies in energy efficiency.

Derived successful transmission probabilities and ergodic service rates.

Numerical results validate theoretical analysis and scheme superiority.

Abstract

By suppressing repeated content deliveries, wireless caching has the potential to substantially improve the energy efficiency (EE) of the fifth generation (5G) communication networks. In this paper, we propose two novel energy-efficient caching schemes in heterogeneous networks, namely, scalable video coding (SVC)-based fractional caching and SVC-based random caching, which can provide on-demand video services with different perceptual qualities. We derive the expressions for successful transmission probabilities and ergodic service rates. Based on the derivations and the established power consumption models, the EE maximization problems are formulated for the two proposed caching schemes. By taking logarithmic approximations of the l0-norm, the problems are efficiently solved by the standard gradient projection method. Numerical results validate the theoretical analysis and demonstrate the superiority of our proposed caching schemes, compared to three benchmark strategies.