The concept of wireless virtualized networks enables Mobile Virtual Network Operators (MVNOs) to utilize resources made available by multiple Infrastructure Providers (InPs) to set up a service. Nevertheless, existing centralized resource provisioning approaches fail to address such a scenario due to conflicting objectives among InPs and their reluctance to share private information. This paper addresses the problem of resource provisioning from several InPs for services with geographic coverage constraints. When complete information is available, an Integer Linear Program (ILP) formulation is provided, along with a greedy solution. An alternative coalition formation approach is then proposed to build coalitions of InPs that satisfy the constraints imposed by an MVNO, while requiring only limited information sharing. The proposed solution adopts a hedonic game-theoretic approach to coalition formation. For each InP, the decision to join or leave a coalition is made in a decentralized manner, relying on the satisfaction of service requirements and on individual profit. Simulation results demonstrate the applicability and performance of the proposed solution.

Network slicing has emerged as a key concept in 5G systems, allowing Mobile Network Operators (MNOs) to build isolated logical networks (slices) on top of shared infrastructure networks managed by Infrastructure Providers (InP). Network slicing requires the assignment of infrastructure network resources to virtual network components at slice activation time and the adjustment of resources for slices under operation. Performing these operations just-in-time, on a best-effort basis, comes with no guarantee on the availability of enough infrastructure resources to meet slice requirements. This paper proposes a prioritized admission control mechanism for concurrent slices based on an infrastructure resource reservation approach. The reservation accounts for the dynamic nature of slice requests while being robust to uncertainties in slice resource demands. Adopting the perspective of an InP, reservation schemes are proposed that maximize the number of slices for which infrastructure resources can be granted while minimizing the costs charged to the MNOs. This requires the solution of a max-min optimization problem with a non-linear cost function and non-linear constraints induced by the robustness to uncertainties of demands and the limitation of the impact of reservation on background services. The cost and the constraints are linearized and several reduced-complexity strategies are proposed to solve the slice admission control and resource reservation problem. Simulations show that the proportion of admitted slices of different priority levels can be adjusted by a differentiated selection of the delay between the reception and the processing instants of a slice resource request.

Network slicing has emerged as a pivotal concept in 5G systems, allowing mobile operators to build isolated logical networks (slices) on top of shared infrastructure networks. Within a network slice, several Service Function Chains are usually deployed on a best-effort premise. Nevertheless, this approach does not guarantee the availability of enough infrastructure resources to accommodate the uncertain and time-varying slice resource demands.This paper investigates two adaptive slice resource provisioning methods accounting for the evolution with time of the slice resource demands. A probabilistic guarantee of meeting the slice resource requirements can be obtained, while being robust against uncertainties. The myopic approach accounts for the past demands when provisioning the current demands, while the foresighted approach accounts for both past and future demands. These two methods lead to MILP problems. Their performance is compared with a quasi-static method, where provisioning is agnostic of the past and future demands.

Network slicing allows Mobile Network Operators to split the physical infrastructure into isolated virtual networks (slices), managed by Service Providers to accommodate customized services. The Service Function Chains (SFCs) belonging to a slice are usually deployed on a best-effort premise: nothing guarantees that network infrastructure resources will be sufficient to support a varying number of users, each with uncertain requirements. Taking the perspective of a network Infrastructure Provider (InP), this article proposes a resource provisioning approach for slices, robust to a partly unknown number of users with random usage of the slice resources. The provisioning scheme aims to maximize the total earnings of the InP, while providing a probabilistic guarantee that the amount of provisioned network resources will meet the slice requirements. Moreover, the proposed provisioning approach is performed so as to limit its impact on low-priority background services, which may co-exist with slices in the infrastructure network. Taking all these constraints into account leads to an integer programming problem with many nonlinear constraints. These constraints are first relaxed to get an integer linear programming formulation of the slice resource provisioning problem. This problem is then solved considering the slice resource provisioning demands jointly. A suboptimal approach is finally proposed where slice resource provisioning demands are considered sequentially. Both solutions are compared to provisioning schemes that do not account for best-effort services sharing the common infrastructure network, as well as uncertainties in the slice resource demands.

With network slicing, Mobile Network Operators can accommodate on a common network infrastructure various customized services from Service Providers (SPs). Usually, the Service Function Chains belonging to a slice are deployed on a best-effort basis. Nothing ensures that enough infrastructure resources can be allocated to satisfy the demands of SPs. This paper introduces a radio resources provisioning approach to satisfy the demands of slices with radio coverage constraints. By provisioning, we ensure that enough resources are reserved for further SFC deployment. Numerical results show the effectiveness of the proposed provisioning framework for a slice deployment on a mobile network infrastructure satisfying a minimum data rate for users in the geographical areas where services have to be made available.

With network slicing in 5G networks, Mobile Network Operators can create various slices for Service Providers (SPs) to accommodate customized services. Usually, the various Service Function Chains (SFCs) belonging to a slice are deployed on a best-effort basis. Nothing ensures that the Infrastructure Provider (InP) will be able to allocate enough resources to cope with the increasing demands of some SP. Moreover, in many situations, slices have to be deployed over some geographical area: coverage as well as minimum per-user rate constraints have then to be taken into account. This paper takes the InP perspective and proposes a slice resource provisioning approach to cope with multiple slice demands in terms of computing, storage, coverage, and rate constraints. The resource requirements of the various SFCs within a slice are aggregated within a graph of Slice Resource Demands (SRD). Infrastructure nodes and links have then to be provisioned so as to satisfy all SRDs. This problem leads to a Mixed Integer Linear Programming formulation. A two-step approach is considered, with several variants, depending on whether the constraints of each slice to be provisioned are taken into account sequentially or jointly. Once provisioning has been performed, any slice deployment strategy may be considered on the reduced-size infrastructure graph on which resources have been provisioned. Simulation results demonstrate the effectiveness of the proposed approach compared to a more classical direct slice embedding approach.

Network slicing has recently appeared as a key enabler for the future 5G networks where Mobile Network Operators (MNO) create various slices for Service Providers (SP) to accommodate customized services. As network slices are operated on a common network infrastructure owned by some Infrastructure Provider (InP), sharing the resources across a set of network slices is highly important for future deployment. In this paper, taking the InP perspective, we propose an optimization framework for slice resource provisioning addressing multiple slice demands in terms of computing, storage, and wireless capacity. We assume that the aggregated resource requirements of the various Service Function Chains to be deployed within a slice may be represented by a graph of slice resource demands. Infrastructure nodes and links have then to be provisioned so as to satisfy these resource demands. A Mixed Integer Linear Programming formulation is considered to address this problem. A realistic use case of slices deployment over a mobile access network is then considered. Simulation results demonstrate the effectiveness of the proposed framework for network slice provisioning.

Future 5G mobile networks are expected to support network slicing, i.e. multiple virtual networks running on a common infrastructure. A virtual network, called network slice, may be created to support a specific communication service type, such as massive machine-type communication, or even a specific application, for example one single Internet of Things (IoT) application. Network slices may be created by an organization like a Mobile Network Operator (MNO) and rented out to 3rd-party organizations like enterprises. A prerequisite for running a highly sensitive service in a network slice is full isolation of the slice against all other users of the common network infrastructure. An industry vertical renting a slice for an Industry 4.0 application may even require isolation against the organizations providing the network infrastructure. In this paper, we introduce different novel provisioning models for 3rd-party slices and discuss their isolation properties. We conclude that network slices relying solely on common infrastructure cannot meet highest isolation requirements. We therefore introduce new network architecture deployment models which include 3rd-party-owned network infrastructure, and show how they can be used by 3rd-parties to implement highly secure mobile communication services.

HTTP adaptive streaming (HAS) has emerged as the main technology for video streaming applications. Multiple HAS video clients sharing the same wireless channel may experience different video qualities as well as different play-out buffer levels, as a result of both different video content complexities and different channel conditions. This causes unfairness in the end-user quality of experience. In this paper, we propose a quality-fair adaptive streaming solution with fair buffer (QFAS-FB) to deliver fair video quality and to achieve asymptotically fair play-out buffer levels among HAS clients competing for the same wireless resources in a long-term evolution (LTE) cell. In the QFAS-FB framework, the share of radio resources is optimized according to video content characteristics, play-out buffer levels, and channel conditions. The proposed solution is compared with the other state-of-the-art strategies, and the numerical results show that it significantly improves the quality fairness among heterogeneous HAS users, reduces the video quality variations, and improves the fairness among the user's play-out buffers.

The Digital Video Broadcasting Satellite services to Handhelds (DVB-SH) is a hybrid satellite/terrestrial system for the broadcasting of multimedia services to mobile receivers. To mitigate deep signal fading events occurring mainly in the land-mobile satellite channel, a Multi-Protocol-Encapsulation Inter-burst Forward Error Correction (MPE-IFEC) technique has been recently introduced. It is based on a clever organization of data and parities offering long interleaving. Two decoding modes have been proposed: the late decoding mode and the early decoding mode. In this paper, we propose to discuss and to analyse the impact of these decoding strate-gies on the channel change delay, known as the key issue of DVB-SH system. Based on an end-to-end DVB-SH network simulator, two criteria: the channel change delay and the cu-mulative freeze time, are computed by considering different MPE-IFEC parameters and typical DVB-SH propagation environments. These two criteria are sufficient to give us the subjective video quality of the newly joined channel.

The DVB-SH system relies on a hybrid satellite/terrestrial infrastructure. A complementary solution using the interworking with a bi-directional path offered by cellular network is under investigation in DVB-Convergence of Broadcast and Mobile Services Group. This aims to provide good QoS of DVB-SH service anywhere anytime. Due to the strict reception conditions and the severe satellite channel propagation, many time-sliced bursts are lost impacting negatively the subjective video quality because the new link layer : inter-burst FEC, fails to recover the burst losses even with an adequate coding rate. In this paper, we propose a repair mechanism and a suitable architecture for real-time streaming service error handling. By recovering only some specific lost information of the broadcasted stream, the quality of TV service is improved.