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Multicasting is becoming more and more important in the Internet of Things (IoT) and wearable applications (e.g., high definition video streaming, virtual reality gaming, public safety, among others) that require high bandwidth efficiency and low energy consumption. In this regard, millimeter wave (mmWave) communications can play a crucial role to efficiently disseminate large volumes of data as well as to enhance the throughput gain in fifth-generation (5G) and beyond networks. There are, however, challenges to face in view of providing multicast services with high data rates under the conditions of short propagation range caused by high path loss at mmWave frequencies. Indeed, the strong directionality required at extremely high frequency bands excludes the possibility of serving all multicast users via a single transmission. Therefore, multicasting in directional systems consists of a sequence of beamformed transmissions to serve all multicast group members, subgroup by subgroup. This paper focuses on multicast data transmission optimization in terms of throughput and, hence, of the energy efficiency of resource-constrained devices such as wearables, running their resource-hungry applications. In particular, we provide a means to perform the beam switching and propose a radio resource management (RRM) policy that can determine the number and width of the beams required to deliver the multicast content to all interested users. Achieved simulation results show that the proposed RRM policy significantly improves network throughput with respect to benchmark approaches. It also achieves a high gain in energy efficiency over unicast and multicast with fixed predefined beams. [ABSTRACT FROM AUTHOR]

There is an important trend in terms of user expectations of ubiquity in relation to rich media services and increase in number and range of devices with high specifications which enable access to such services, with very ambitious technical requirements. Providing this support is a very challenging issue, especially in urban dense network environments (DenseNet). Diverse solutions have been proposed, including deployment of femtocells in conjunction with the existing infrastructure, but there is still need for new approaches to balance resources and quality in current competitive market. This paper proposes an innovative three-phase adaptive scheduling solution [energy-quality utility-based adaptive scheduling solution (EQUAS)], which performs trade off between service quality and energy efficiency when allocating network resources to mobile users in a DenseNet. Resource allocation is performed according to a utility function that takes into account throughput, device energy consumption, and user mobility. Furthermore, adaptive reallocation is applied to increase network coverage and avoid dropping service. Testing results show how EQUAS outperforms two competitive approaches in terms of energy consumption and efficiency, data throughput, and estimated user satisfaction. [ABSTRACT FROM AUTHOR]

Resource management in emerging dense heterogeneous network environments (DenseNets) is a challenging issue. The employment of multicast transmissions in this scenario has potential to address the problems. On one hand, the large number of smart user mobile devices and user expectations for high-quality rich media services has determined a growing demand for network resources; in DenseNets, mobile users have to make the choice in terms of the network to connect to, in order to balance energy saving and delivery performance. On the other hand, the proliferation of user accesses to the existing and future network infrastructure will bring along with it the operators need for optimizing the radio resource usage. This paper proposes a hybrid unicast-multicast utility-based network selection algorithm (HUMANS), which offers the additional option of selecting multicast transmissions in the network selection process during video delivery. By serving users with good channel conditions via unicast transmissions and users with poor channel quality conditions via multicast, HUMANS allows outperforming other solutions in terms of outage percentage and average quality of transmission, in both low- and high-density scenarios. Most importantly, at the same time it guarantees operators a more efficient resource utilization. [ABSTRACT FROM AUTHOR]