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Network Function Virtualization (NFV)-based 5G networks deliver specific services to a end-users through the creation of Service Function Chains (SFCs), which are composed of a number of Virtualized Network Functions (VNFs) interconnected via a set of virtual links (vLinks). VNFs consume computational resources of the network's servers, while vLinks utilize the communicational resources of the network. The efficient utilization of network resources remains a challenge in NFV-based networks. To this end, in this paper, we propose an analytical framework for the Admission Ratio (AR) calculation in NFV-based converged optical-wireless 5G networks. The proposed methodology employs a network slicing architecture in which different network slices form end-to-end logically isolated networks and each slice delivers a specific service type to the users through its SFC(s). In the proposed analysis, we not only take into account the occupancy distribution in both the network's computational and communicational domains (servers and fiber links), but we also consider the SFC establishment AR by taking into account different sub-service-classes belonging to different slices. The accuracy of the model is evaluated through the comparison of analytical and simulation results and was found satisfactory. Furthermore, the proposed model is employed for the determination of the optimal (minimum) capacity for all SFCs elements (VNFs and vLinks), in a way that users belonging to a specific slice experience a predefined value of AR as minimum. Additionally, our calculations deploy recursive formulas, which have a low computational complexity, as opposed to time-consuming simulations, without requiring the application of complex optimization algorithms. [ABSTRACT FROM AUTHOR]

In this correspondence paper, an underlay cognitive radio multicast network, consisting of a cognitive base station (CBS) and multiple multicast groups of secondary users (SUs), is considered. All SUs, belonging to a particular multicast group, are served by the CBS using a common primary user (PU) channel. The goal is to maximize the energy efficiency (EE) of the system, through dynamic adaptation of target rate and transmit power for each multicast group, under the PUs’ individual interference constraint. The optimization problem formulated for this is proved to be nonquasi-concave with respect to the joint variation of the CBS's transmit power and target rate. An efficient iterative algorithm for EE maximization is proposed along with its complexity analysis. Simulation results illustrate the performance gain of our proposed scheme. [ABSTRACT FROM AUTHOR]

Dynamic Adaptive Streaming over HTTP (DASH) is a fast growing video streaming platform, which enables adaptive rate selection based on channel conditions. File Delivery over Unidirectional Transport (FLUTE) further enables multicasting of the DASH segments over LTE eMBMS systems. In this paper, an optimal DASH-multicasting solution is proposed to allow more DASH clients in an LTE network to receive better videos by optimizing the resource allocation, application-layer forward error correction (FEC) code rate and modulation and coding scheme (MCS) selection of each multicasting group, which corresponds to a FLUTE session. Multiple FLUTE sessions are considered to deliver multiple videos, each with different video rates, for enhancing the overall utility. We have applied the convex optimization method to find the optimal resource allocation in terms of utility for multiple FLUTE sessions. We also find the optimal FEC code rates to add redundancies to protect the video segments for each FLUTE session. Moreover, an efficient MCS selection is introduced to reduce the complexity of the algorithm. Simulation results, with realistic LTE parameters, are shown to prove the proposed scheme is optimal, with more DASH clients receiving better video representations within limited resources when compared to other existing algorithms. [ABSTRACT FROM PUBLISHER]