A Performance Study of a Network Selection Algorithm Employing Multicast Transmissions In 5G networks context, both multicast transmissions and dense heterogeneous networks (DenseNet) will be beneficial for improving resource utilization, and increasing coverage and system capacity. Nevertheless, they raise several issues about interference coordination, power consumption, radio resource management and load balancing. In this paper, multicast transmissions are being used in a DenseNet scenario to complement unicast services and support a qualityaware network selection process. In particular, the proposed HUMANS algorithm has been tailored for highquality video services, which require high datarates and high bandwidth utilization. The analysis carried out shows improved performance when employing HUMANS when varying its different weights assigned to the different parameters in order to achieve its goal.

Additive Logarithmic Weighting HetNet Selection Method for Video Services over 5G Networks 5thGeneration (5G) applications require high data rates and Quality of Service (QoS) levels. Mobile devices can access the Internet exploiting several technologies, as for example Long Term Evolution (LTE) or Wireless Fidelity (WiFi). In this context, the heterogeneous network (HetNet) paradigm has been introduced to allow the coexistence of devices and services with different characteristics assuring a high QoS level to users without modifying resource distribution and management. Several access network selection methods have been proposed showing the limitation of not considering both the type of traffic for the service requested by users and the characteristics of devices. To overcome this limitation, a new HetNet selection method based on additive logarithmic weighting (ALW) algorithm is presented in this paper, combining information related to the received signal power, network load, and mobility of users.