Resource Allocation Schemes for Revenue Maximization in Multicast D2D Networks

Abstract

Mobile users' service satisfaction plays a vital role in improving the revenue of telecom companies. When user data demands are uniform, satisfying the user requests is much easier than when demands are diverse. Yet, in a multicast scenario, each user may wish to view the same multicast data at different bit rates based on their own device capacities or resolutions. In this paper, we consider device-to-device (D2D) multicast users who may demand the multicast data at various rates, and in return, they offer different profits (revenue) to the telecom operator. Moreover, users may have different channel qualities from the base station, which will also affect the data rates. We show that satisfying the user requests to maximize the profit becomes NP-hard when the resource blocks are limited, and propose a greedy heuristic and two approximation algorithms to solve this problem. Besides, we consider an alternative objective of maximizing the number of satisfied users and propose a greedy heuristic algorithm for this variant. Our simulation results demonstrate that the proposed algorithms offer higher profit, throughput, and satisfy more users than the other candidate algorithms.

Keywords

• Approximation algorithm
• device-to-device multicast
• profit
• satisfied user
• service satisfaction