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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]

mcMTC is starting to play a central role in the industrial Internet of Things ecosystem and have the potential to create high-revenue businesses, including intelligent transportation systems, energy/ smart grid control, public safety services, and high-end wearable applications. Consequently, in the 5G of wireless networks, mcMTC have imposed a wide range of requirements on the enabling technology, such as low power, high reliability, and low latency connectivity. Recognizing these challenges, the recent and ongoing releases of LTE systems incorporate support for lowcost and enhanced coverage, reduced latency, and high reliability for devices at varying levels of mobility. In this article, we examine the effects of heterogeneous user and device mobility -- produced by a mixture of various mobility patterns -- on the performance of mcMTC across three representative scenarios within a multi-connectivity 5G network. We establish that the availability of alternative connectivity options, such as D2D links and drone-assisted access, helps meet the requirements of mcMTC applications in a wide range of scenarios, including industrial automation, vehicular connectivity, and urban communications. In particular, we confirm improvements of up to 40 percent in link availability and reliability with the use of proximate connections on top of the cellular-only baseline. [ABSTRACT FROM AUTHOR]

Fifth Generation (5G) wireless systems are expected to connect an avalanche of "smart" objects disseminated from the largest "Smart City" to the smallest "Smart Home". In this vision, Long Term Evolution-Advanced (LTE-A) is deemed to play a fundamental role in the Internet of Things (IoT) arena providing a large coherent infrastructure and a wide wireless connectivity to the devices. However, since LTE-A was originally designed to support high data rates and large data size, novel solutions are required to enable an efficient use of radio resources to convey small data packets typically exchanged by IoT applications in "smart" environments. On the other hand, the typically high energy consumption required by cellular communications is a serious obstacle to large scale IoT deployments under cellular connectivity as in the case of Smart City scenarios. Network-assisted Device-to-Device (D2D) communications are considered as a viable solution to reduce the energy consumption for the devices. The particular approach presented in this paper consists in appointing one of the IoT smart devices as a collector of all data from a cluster of objects using D2D links, thus acting as an aggregator toward the eNodeB. By smartly adapting the Modulation and Coding Scheme (MCS) on the communication links, we will show it is possible to maximize the radio resource utilization as a function of the total amount of data to be sent. A further benefit that we will highlight is the possibility to reduce the transmission power when a more robust MCS is adopted. A comprehensive performance evaluation in a wide set of scenarios will testify the achievable gains in terms of energy efficiency and resource utilization in the envisaged D2D-based IoT data collection. [ABSTRACT FROM AUTHOR]

The growing demand for mobile multicast services, such as Internet Protocol television (IPTV) and video streaming, requires effective radio resource management (RRM) to handle traffic with strict quality-of-service constraints over Long-Term Evolution (LTE) and beyond systems. Special care is needed to limit system performance degradation when multiple multicast streams are simultaneously transmitted. To this aim, this paper proposes an RRM policy based on a subgrouping technique for the delivery of scalable multicast video flows in a cell. Our proposal enhances the legacy multicast transmission over LTE systems by exploiting multiuser diversity and the users' channel quality feedbacks. Moreover, it is designed to take advantage of the frequency selectivity in the subgroup formation. Simulation results demonstrate the effectiveness of the proposed scheme, which outperforms existing approaches from the literature. It succeeds in achieving higher spectral efficiency and guaranteeing adequate video quality to all multicast receivers and improved quality to those with good channel conditions. [ABSTRACT FROM AUTHOR]

The objective of this paper is to design radio resource management policies for supporting the effective delivery of multicast service in Long-Term Evolution Advanced (LTE-A) systems. Specifically, we propose multicast subgrouping-based solutions to maximize the system efficiency in terms of throughput under the constraints that 1) all user equipment devices are served and that 2) fairness among the activated multicast subgroups is guaranteed to avoid users' dissatisfaction. These results are obtained by modeling the multicast subgroup formation problem through a bargaining game. Moreover, an algorithm, which achieves the optimal fairness–efficiency tradeoff, is proposed to support the system in selecting the most appropriate solution in terms of 1) subgroups activated over each system carrier and 2) resource allocated to them. [ABSTRACT FROM PUBLISHER]