EMB: Efficient Multimedia Broadcast in Multi-tier Mobile Networks

Multimedia broadcast and multicast services (MBMS) in mobile networks has been widely addressed, however an investigation of such a technology in emerging, multi-tier, scenarios is still lacking. Notably, user clustering and resource allocation are extremely challenging in multi-tier networks, and imperative to maximize system capacity and improve quality of user-experience (QoE) in MBMS. Thus, in this paper we propose a clustering and resource allocation approach, named EMB, which specifically addresses heterogeneous networks and accounts for the fact that multimedia content is adaptively encoded into scalable layers depending on the QoE requirements and channel conditions of the heterogeneous users. Importantly, we prove that our clustering algorithm yields Pareto efficient broadcasting areas, multimedia encoding parameters, and re- source allocation, in a way that is also fair to the users. Fur- thermore, numerical results obtained under realistic conditions and using real-world video content, show that the proposed EMB results in lower churn count (i.e., higher number of served users), higher throughput, and increased QoE, while using fewer network resources

Energy-aware clustering for multi-cell joint transmission in LTE networks

This paper investigates the energy-aware clustering of cooperating base stations in the downlink of cellular networks. The focus of this work is on static clustering deployments for LTE systems when joint signal precoding is employed at multiple base stations. We demonstrate that properly planned clustering can provide the desired balance between network spectral and energy efficiency. To this end, we compare the overall energy consumption of various clustered cooperation layouts while considering different target performance metrics at user end. Our evaluations for various inter-site distance deployments in a practical macrocell scenario unveil the individual parameters controlling the energy effectiveness of a clustering strategy. In fact, it is shown that the choice of the optimum clustering layout depends on: 1) the specific service demands; 2) the deployment density of the network and; 3) on the ability of the base stations to jointly adjust their transmit power. Ultimately, we provide a general framework for choosing the most appropriate cooperation set of base stations in energy-aware networks