Delivering massive video content while consuming low energy at the user devices is an issue of increasing importance, both for the users and operators. In this paper we consider the problem of minimum-energy video delivery when the users are served by LTE multicast transmissions which are assisted by Wi-Fi networks. We formulate the optimization problem where we jointly decide the user association and spectrum allocation to multicast groups, and the Wi-Fi access point (AP) association and airtime allocation to users, in order to deliver a requested video quality. The problem is NP-hard and we propose a cutting planes algorithm, based on Benders decomposition, to expedite its exact solution. We evaluate our proposal using a wealth of simulation experiments based on 3GPP parameters and measurement studies. Our findings show that coordinated decisions on both networks reduce the devices power consumption by up to approximately 50%.

360° video streaming is rapidly progressing towards the multimedia industry, offering an interactive user experience but also challenging telco operators due to the huge volume of the video data and the user channel dynamics. We propose a software defined transport layer proxy overlay architecture, that builds on top of existing standard technologies, to facilitate hybrid multicast/unicast 360° video delivery as a service-particularly combining LTE multicast and WiFi unicast transmissions. In addition we holistically define the emergent optimization problem, where we jointly consider network association, multicast/unicast scheduling, and spectrum/bandwidth management in both networks, taking also into consideration user specific preferences, e.g., the popularity of the video tiles. The proposed framework is evaluated through a series of simulation based experiments following 3GPP standard parameters and real 360° video traces. Our findings reveal significantly increase in the user utility, particularly when the wireless spectrum is limited.

Mobile network operators today need to deliver new types of multimedia content, such as 360° video, to an ever-growing population of users. This creates a pressing need for network mechanisms that can support such demanding services in an economically-efficient fashion. This work focuses on applications that deliver a video file concurrently to multiple users which can utilize different radio technologies, namely cellular and Wi-Fi networks. We propose a mechanism for the orchestration of LTE multicast and Wi-Fi unicast transmissions by optimizing jointly: the design of multicast groups, the spectrum and bandwidth management in both networks, and the video encoding quality for each user (or, group). We consider two key performance criteria: minimizing the utilized LTE spectrum resources, and maximizing the delivered video quality. We formulate the respective optimization problems, prove they are NP-hard, and design an exact algorithm for solving them in near-real time. We employ a wealth of simulation experiments, using 3GPP-compliant parameters, that compare our approach with state-of-the-art benchmarks. Our findings suggest that, in representative scenarios, this multi-RAT orchestration can save up 55% LTE radio resources and increase up to 46% the delivered video quality.

Cloud-RAN based architectures are widely considered a fundamental part of 5G networks. As a consequence, in the upcoming standards for 5G RAN, disaggregating the RAN functionality between a Central Unit (CU) and multiple Distributed Units (DUs) is considered, addressing the splitting of the 5G protocol stack at the PDCP/RLC point. This split is expected to bring numerous advantages to mobile network operators, as through the isolation of the stack from the PDCP layer and upwards, the CU will be able to act as the Cloud-based convergence point among multiple heterogeneous technologies in the provisioned networks and hence able to serve multiple heterogeneous DUs. Moreover, data rate requirements for this type of split are not very demanding, thus allowing the IP-based transferring of data from the DU to CU and vice-versa. In this work, we propose, implement and evaluate a protocol for a Cloud-RAN based architecture allowing the selection and dynamic switching of different heterogeneous networks in the RAN. We rely on the open source OpenAirInterface platform and extend it to support data plane splitting of the LTE functionality, and the subsequent data injection to WiFi networks. We evaluate the platform using a real network setup, under several scenarios of network selection and different delay settings.

Centralized RAN processing has been identified as one of the major enablers for 5G mobile network access. By moving the baseband units (BBU) to the Cloud, multiple instances can be instantiated on the fly, serving several Remote Radio Head (RRH) units. The goal is to satisfy the existing demand of particular geographical areas, whereas drastically reducing the overall CAPEX and OPEX costs of the mobile operators. In this work, we present an experimental study of real Cloud-RAN deployments, with respect to different functional splits. We use as a reference architecture the 3GPP LTE stack, and argue about the functional split applicability in contemporary networks. We evaluate Layer 2 functional splits, that can be used for the convergence of multiple heterogeneous wireless technologies in an all-in-one unit. By deploying our approach in a real testbed setup, we extract the backhaul network transfer requirements for the different splits and present our experimental findings, compared with the respective simulation results.

With the emergence of Online Social Networks (OSNs), as the most popular medium for advertisements, as source of knowledge and information, the emergence of malicious contents (viruses, false rumors, etc..) has become a critical issue that requires immediate attention. In this study we investigate on blocking the contagion of malicious things dynamically, by continuously fighting the diffusion near the source of misinformation under the Susceptible-Infectious-Recovered (SIR) model. We focus on protecting networked populations by removing key connections between nodes, and show via experimental results, that by following the infection the contagion can be controlled more efficiently and even being stopped in the earliest steps. We modify a well studied heuristic from the literature of graphs, and show that our proposed technique significantly outperforms what we believe the state-of-the-art competitors by successfully confronting the infection in real networks.