While the integration of the Internet of Things (IoT) has expanded networking capabilities, it has also intensified challenges regarding communication reliability and resource constraints in wireless environments. Interference and message collisions significantly impact fault tolerance and energy efficiency, particularly in distance-2 colored networks. While traditional TDMA-based MAC schemes mitigate collisions, they often leave a gap in performance due to underutilized time slots. This paper bridges that gap by unveiling an incisive study on harnessing underused time slots to bolster both communication and energy efficiency in wireless networks. Our new distributed protocol—operating within the synchronous distance-2 colored broadcast/receive model—empowers highly active nodes to judiciously borrow time slots from underutilized neighbors while preserving collision-free and conflict-free guarantees. Furthermore, we introduce a nuanced probability-based strategy that enables nodes to discern opportune moments for lending their time slots. Experimental results substantiate the efficacy of our proposed solution, offering a pathway toward optimizing wireless network operations in the IoT era.

In large-scale resource-constrained wireless networks, such as those prevalent in the Internet of Things (IoT), efficient communication scheduling remains a critical challenge. Among the various approaches, Time Division Multiple Access (TDMA) protocols have been widely adopted for their structured and collision-free communication capabilities. Nevertheless, despite extensive research in this area, current solutions often exhibit suboptimal performance, particularly in dynamic environments where node activity levels fluctuate over time.This paper introduces a novel fully distributed TDMA-based scheduling protocol that intelligently maximizes the utilization of communication resources. The proposed approach adaptively reallocates underutilized time slots, originally assigned to temporarily inactive nodes, to those experiencing higher communication demands. This dynamic reallocation not only improves channel utilization but also reduces idle periods, thereby enhancing overall network efficiency. To further enhance performance, we incorporate a lightweight probabilistic mechanism that governs the temporal leasing of unused slots. This mechanism balances the trade-off between slot availability and transmission reliability, minimizing packet loss while preserving fairness and stability within the network.Simulations across a range of network scenarios demonstrate that our protocol significantly improves throughput, latency, and reliability in resource-constrained environments. These results highlight the protocol’s potential as a robust and scalable solution for adaptive and energy-efficient scheduling in next-generation IoT networks.

This paper presents a novel enhancement to Single Packet Authorization (SPA), namely Moving-target SPA (mSPA), that strengthens network security by leveraging moving target defense to enable dynamic port management and service concealment in softwarized networks. Traditional techniques are limited in their ability to mask services effectively, leaving them vulnerable to attacks such as port scanning, sniffing, and traffic analysis. Our solution addresses these limitations by introducing a SPA response validation to ensure that single packet authorization is correctly processed and the access port, managed dynamically, is only granted to legitimate users, thereby preventing unauthorized access and enhancing overall security. This strategy aligns with modern security paradigms, such as Zero Trust Architecture, and addresses the need for scalable, lightweight solutions in IoT environments, where dynamic and resource-constrained systems face significant security challenges. We validate the mSPA approach using a real experiment. The results suggest that our approach is scalable and effective in masking and protecting network services by minimizing the visibility of services and reducing the attack surface.

Individuals with reduced mobility rely on accurate information to navigate transit systems effectively. Proactive notifications about Train Schedule Variability (TSV) can significantly enhance their travel experience by providing timely alerts on potential delays. This paper introduces a three-layer Long Short-Term Memory (LSTM) model to predict TSV and notify reduced-mobility individuals through a Human-Machine Interface (HMI) system integrated into their wheelchairs. Our model estimates a Variability Index (VI) over 7, 15, and 30-day windows within a hierarchical architecture spanning edge, fog, and cloud layers. The edge layer delivers advance notifications, alerting users up to 7 days prior to anticipated delays. A Hierarchical Clustering Approach (HCA) is applied for robust data preparation, addressing missing values and identifying weekly patterns in a one-year dataset of train schedules from Frankfurt Station. This dataset, inclusive of seasonal and weather variability, undergoes additional processing with median filters and time-sliding windows to create data matrices. We evaluate the proposed three-layer LSTM model against multiple LSTM configurations and traditional time series forecasting models, including AutoRegressive Integrated Moving Average (ARIMA), Kalman Filtering, and an ARIMA-Kalman hybrid. Model performance is assessed using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Error (MAE). Additionally, results demonstrate that the Variability Assignment Score (VAS) surpasses Mean Squared Error (MSE) in fault detection accuracy. Receiver Operating Characteristic (ROC) curve analysis, using Area Under the Curve (AUC) metrics, validates the superior performance of our model. Specifically, the VAS-based ROC curve for the three-layer LSTM model outperforms others, underscoring VAS as a more effective measure for variability detection. Further validation via the Diebold-Mariano (DM) test confirms the suitability of the three-layer LSTM model for this application.

Toll management systems have a vital role in facilitating smooth and effective traffic flow at toll stations. To this end, numerous use cases have been explored to guide and detect vehicles at toll stations. This paper presents the results of a study conducted on a testbed as part of the European project InDiD. By utilizing more robust technologies, our work aims to enhance a specific use case explored within this project. Leveraging the resources of the APRR (Autoroute Paris-Rhin-Rhône enhanced) Cooperative Intelligent Transportation System (C-ITS) testbed, we did two experiments to assess the use of two technologies in improving vehicles orientation while approaching a toll station. The first one featured an upgraded vehicular communication network, incorporating both ITS-G5 and LTE-UU to improve the reliability of the vehicular communications. The subsequent one integrated computational power directly onto the vehicle (vMEC) to compare it with the use of an edge computing resource. The results show an improvement in reliability, a coverage extension, and a favorable latency tradeoff when using the vMEC. Additionally, we closely monitored the downlink resource consumption while employing hybrid communication.

Multiaccess edge computing (MEC) has emerged as a promising technology for time-sensitive and computation-intensive tasks. However, user mobility, particularly in vehicular networks, and limited coverage of Edge Server result in service interruptions and a decrease in Quality of Service (QoS). Service migration has the potential to effectively resolve this issue. In this paper, we investigate the problem of service migration in a MEC-enabled vehicular network to minimize the total service latency and migration cost. To this end, we formulate the service migration problem as a Markov decision process (MDP). We present novel contributions by providing optimal adaptive migration strategies which consider vehicle mobility, server load, and different service profiles. We solve the problem using the Double Deep Q-network algorithm (DDQN). Simulation results show that the proposed DDQN scheme achieves a better tradeoff between latency and migration cost compared with other approaches.

Cooperative intelligent transportation systems are now being widely investigated along with the emergence of vehicular communication. Services such as collective perception require a robust communication system with high throughput and reliability. However, a single communication technology is unlikely to support the required throughput, especially under mobility and coverage constraints. Thus, we propose in this paper a hybrid vehicular communication architecture that leverages multiple Radio Access Technologies (RATs) to enhance the communication throughput. We developed a Deep Reinforcement Learning (DRL) algorithm to select the optimal hybrid transmission strategy according to the channel quality parameters. We assess the effectiveness of our hybrid transmission strategy by a simulation scenario that shows about 20% throughput enhancement and a 10% reduction of channel busy ratio.

Recent advancements in autonomous vehicle perception haveexposed limitations of onboard sensors such as radar, lidar, and cameras, which road obstacles and adverse weather conditions can impede. Connected and Autonomous Vehicles (CAVs) are leveraging wireless communications to share perception information through a process called Cooperative Perception (CP), aiming to provide a more comprehensive understanding of their environment. However, this can result in excessive redundant and useless information in the network, as the same road objects may be detected and exchanged simultaneously by multiple CAVs. This not only consumes more network resources but also may overload the communication channel, reducing the delivery of perception information to CAVs and ultimately decreasing the overall CP awareness in the network. This paper introduces MCORM, a multi-agent learning method based on the advantage actor-critic algorithm to maximize object usefulness and reduce redundancy in the network. Our evaluations demonstrate that through this method, CAVs learn optimal CP message content selection policies that maximize usefulness. Further more, our proposal proves to be more effective in mitigating object redundancy and improving network reliability in comparison to existing approaches.

With an increasing number of vehicles equipped with ACC (Adaptive Cruise Control), the impact of such vehicles on the traffic flow becomes significant. Especially in the zones where traffic safety is a concern. Here, we can mention roads where traffic is characterized by its "stop and go" particularity. One of the widely used and well-known algorithms to serve as the basis for a control algorithm of a real-world ACC system is the IDM (Intelligent Driver Model). This contribution analyzes the IDM, a CFM (Car-Following Model) governed by a system of differential equations. The model, in recent years, has been intensively studied for capturing traffic phenomena properly and making driver braking behavior safer. Despite the intensive analyses, to our knowledge, a rigorous study of comfortable braking, when an emergency occurs, has rarely been performed. Especially when vehicles' densities and speeds are in constant variation and when, in most situations, urgent braking is necessary. In this work, we modify the classic IDM to introduce the O-IDM (Optimized-Intelligent Driver Model). We increase safety distances between vehicles to prevent accident risks without permitting vehicles' velocities to become negative or to diverge to -∞ in finite time. We personalize IDM parameters for each type of vehicle and we aim to make the transition of the traffic flow from "free flow" to "stop and go" traffic more comfortable. We compare the obtained O-IDM results to the Krauss-FM (Krauss-Following Model) and to the classic IDM to prove the efficiency of our approach.

Cooperative intelligent transport systems rely on a set of Vehicle-to-Everything (V2X) applications to enhance road safety. Emerging new V2X applications like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) applications depend on a significant amount of shared data and require high reliability, low end-to-end (E2E) latency, and high throughput. However, present V2X communication technologies such as ITS-G5 and C-V2X (Cellular V2X) cannot satisfy these requirements alone. In this paper, we propose an intelligent, scalable hybrid vehicular communication architecture that leverages the performance of multiple Radio Access Technologies (RATs) to meet the needs of these applications. Then, we propose a communication mode selection algorithm based on Deep Reinforcement Learning (DRL) to maximize the network's reliability while limiting resource consumption. Finally, we assess our work using the platooning scenario that requires high reliability. Numerical results reveal that the hybrid vehicular communication architecture has the potential to enhance the packet reception rate (PRR) by up to 30% compared to both the static RAT selection strategy and the multi-criteria decision-making (MCDM) selection algorithm. Additionally, it improves the efficiency of the redundant communication mode by 20% regarding resource consumption.

The use of Cooperative Perception (CP) enables Connected and Autonomous Vehicles (CAVs) to exchange objects perceived from onboard sensors (e.g., radars, lidars, and cameras) with other CAVs via CP messages (CPMs) through Vehicle-to-Vehicle (V2V) communication technologies. However, the same objects in the driving environment may simultaneously appear in the line of sight of multiple CAVs. Consequently, this leads to much irrelevant and redundant information being exchanged in the V2V network. This overloads the communication channel and reduces the CPM delivery to CAVs, thereby decreasing CP awareness. To address this issue, we mathematically formulate CP information usefulness as a maximization problem in a multi-CAV environment and introduce a distributed multi-agent deep reinforcement learning approach based on the double deep Q-learning algorithm to solve it. This approach allows each CAV to learn an optimal CPM content selection policy that maximizes the usefulness of surrounding CAVs as much as possible to reduce redundancy in the V2V network. Simulation results highlight that the proposal effectively mitigates object redundancy and improves network reliability, ensuring increased awareness at short and medium distances of less than 200 m compared to state-of-the-art approaches.

Emerging V2X applications, like Advanced Driver Assistance Systems (ADASs) and Connected Autonomous Driving (CAD) require Ultra-Reliable Low Latency Communications (URLLC). Unfortunately, none of the existing V2X communication technologies, such as ETSI ITS-G5 or C-V2X (Cellular V2X including 5G NR), can satisfy these requirements independently. In this paper, we propose a scalable hybrid vehicular communication architecture that leverages the performance of multiple Radio Access Technologies (RATs). To this purpose, we propose a novel ITS station protocol stack and a decentralized RAT selection strategy that uses Deep Reinforcement Learning (DRL). The proposed approach employs a double deep Q-learning (DDQN) algorithm that allows each vehicle to determine the optimal RAT combination to meet the specific needs of the V2X application while limiting resource consumption and channel load. Furthermore, we assess the ability of our architecture to offer reliable and high throughput communication in two different scenarios with varying traffic flow densities. Numerical results reveal that the hybrid vehicular communication architecture has the potential to enhance the packet reception rate (PRR) by up to 30% compared to both the static RAT selection strategy and the multi-criteria decision-making (MCDM) selection algorithm. Additionally, the selection strategy exhibits about a 20% improvement in throughput and a 10% reduction in the channel busy ratio (CBR).

The automotive market is undergoing revolutionary technological transformations, leading to today’s "Cooperative Intelligent Transport System" or C-ITS. This latter raises challenging demands in terms of coverage and connectivity to ensure ultra-low latency and ultra-high reliability under high mobility and density conditions. This work focuses on the C-ITS standardized by ETSI, and whose access network relies on the ITS-G5 technology to achieve V2X services. This paper proposes an ITS-G5 RAN slicing architecture that integrates new modules via the ITS protocol stack to create multiple RAN slices with different priorities and provide secure access to a given slice while performing traffic shaping and policing as well as traffic prioritization and isolation between different slices.

Cooperative Perception (CP) allows Connected and Autonomous Vehicles (CAVs) to enhance their Environmental Awareness (EA) by sharing locally perceived objects through CP messages (CPMs). European Telecommunications Standards Institute (ETSI) has recently defined a set of CPM generation rules to achieve a trade-off between EA and Channel Busy Ratio (CBR) despite massive perception data. Nonetheless, these rules still lack the consideration of information usefulness, resulting in a considerable volume of useless information transmitted in the CP network. This limitation could increase CBR and thus decrease EA due to the loss of CPMs in the network. This paper introduces CloudAC-IU, a cloud-based deep reinforcement learning approach to lean CAVs to maximize perception information usefulness in the network. Simulation results highlight that the CloudAC-IU enhances EA by decreasing CBR and increasing CPM reception for CAVs compared to state-of-the-art works.

Vehicular applications in smart cities, such as assisted and autonomous driving, require sophisticated data processing, low latency, and high throughput data transmission. Edge Computing is a leading approach designed to meet those application requirements. By deploying Edge servers at the network's edge, close to the vehicles, such applications can be successfully delivered while adhering to low-latency and high-throughput requirements. However, optimal placement of Edge servers is challenging since it necessitates a trade-off between quality of service and deployment cost. Latency can be reduced by placing as many Edge servers as feasible close to the vehicles, however, this results in significant deployment costs. This work addresses the problem of optimal Edge server placement. It solves this problem using integer linear programming, considering the relation between delay and cost, as well as the capacity of Edge servers in realistic road traffic scenarios. The proposed generic methodology is designed to reduce the cost of deploying Edge servers by combining the achievement of the desired latency threshold with workload balancing between Edge servers. We evaluate the efficiency of the proposed solution mathematically and through simulations based on open data from real vehicles traffic on roadways of Bordeaux, France. The obtained results demonstrate that our solution outperforms existing Edge server placement approaches, especially on workload balancing.

Vehicle density and channel utilization can vary significantly over short time intervals in complex and dynamic environments such as vehicular networks. In such situations, the European ITS-G5 technology proposed Decentralized Congestion Control (DCC) techniques, such as the reactive Transmit Rate Control (TRC) and the adaptive Dual α algorithms, to prevent performance degradation. This paper proposes a proactive DCC technique that uses a Recurrent Neural Network (RNN), namely the Long Short-Term Memory (LSTM), to optimize the channel performance. We specifically use smoothed Channel Busy Ratio (CBR) forecasting time series calculated using an LSTM agent and provided as inputs to DCC algorithms to achieve faster channel load convergence and, as a result, improve network stability, channel load limitation, and resource allocation fairness. According to the simulation results, the proposed proactive DCC algorithms outperform existing algorithms in terms of reduction in average channel load and faster channel convergence.

Network Function Virtualization (NFV) has emerged as a disruptive networking architecture whose galloping evolution is prompting enterprises to outsource network functions to the cloud and ultimately harvest the fruits of cloud computing, including elasticity, pay-as-you-go billing model, and on-demand services provisioning. However, many reluctant enterprises oppose the benefits of this outsourcing to their critical and pressing concerns about security, trust, and compliance. The latter anticipate possible security and QoS policy violations stemming from dishonest behaviors by cloud providers, attacks by co-resident competitors, misconfiguration by cloud administrators, or implementations flaws by NFV developers. As a result, migrating sensitive workloads to the cloud requires enterprises to first assess risks by gaining knowledge of possible network services’ anomalies and second, to build trust in the cloud by designing effective mechanisms to detect such anomalies. This survey provides scrutiny of network services anomalies that may occur in the NFV environments. We first present a taxonomy of network service anomalies and analyze their negative impacts on critical service attributes, including security and performance. Second, we compare and classify the existing anomalies’ verification mechanisms from the literature. Finally, we point out the literature gap and identify future research directions for anomalies verification in NFV.

Cooperative Intelligent Transport Systems (C-ITS) are built based on vehicular communication technologies with real-time information exchange to improve overall traffic management such as road safety, efficiency, and comfort. Road safety depends on several factors that should be considered, among which the reliability of the used in-vehicle awareness systems. Therefore, communication technologies are rapidly evolving, and New Radio Access Technologies (RATs) are emerging to provide enhanced performances in terms of reliability, coverage, and throughput. C-ITS can benefit from these new RATs enhancements to allow new use cases and applications and then prevent additional road accidents. In this context, the IEEE 802.11bd and cellular vehicle-to-everything (C-V2X) technologies such as LTE-V2X and NR V2X are being developed. This advance is fundamentally reshaping the C-ITS landscape as both technologies (IEEE 802.11bd and LTE-V2X) are competing. In this paper, we analyze how these technologies help increase road safety by making communication more reliable. Furthermore, we will compare their performance using our IEEE 802.11bd implementation in OMNeT++ in terms of packet reception ratio. Finally, we will forecast the number of avoided serious injuries on the European roads.

Cooperative Intelligent Transport Systems (C-ITS) are becoming indispensable in improving road safety and traffic management. ITS-G5 is the European standard for vehicle to everything (V2X) communications. With advanced V2X applications and use cases, the need for enhanced communication performance and broader perception of the road becomes crucial, particularly in congested areas, where the communication performance decreases significantly. Network slicing is a new network paradigm for next-generation networks. It has proved useful to guarantee the quality-of-service requirements of various applications. This paper presents an end-to-end network slicing for the ITS-G5 vehicular communications where slices' resources are orchestrated on all the ITS infrastructure, including the ITS-G5 radio access network. The proposed slicing mechanism aims to provide enhanced end-to-end (E2E) latency for specific traffic types and user groups. We demonstrate the slicing mechanism's effectiveness on two different priority groups carried in two different slices (i.e., lower-priority slice and higher-priority slice). Simulation results show a significant improvement of the E2E latency for users of the higher-priority slice.

Cooperative Intelligent Transportation Systems (C-ITS) are being developed to enable information exchange among vehicles and between vehicles and the roadside infrastructure. C-ITS are regarded as a base technology to reduce road accidents and improve traffic efficiency sustainably. Driving in unsignalized intersections is a significant problem for drivers due to many accidents registered on such roads. In this paper, we aim to solve this problem by proposing an Intersection Assistance System (IAS) that helps C-ITS drivers crossing the intersection more safely. Our solution, namely ESIAS (Edge-based Safety Intersection Assistance System), uses an edge-computing architecture to process efficiently the data offloaded by sensors (cameras/lidars) installed on road intersections. Our solution's performance is evaluated through different simulation scenarios by calculating the number of avoided accidents in each scenario. The results show that ESIAS can effectively reduce 80% of potential accidents.

Cloud computing and cloud-native applications have become standards for new developments in most organizations. Many companies are moving their workload toward microservices and profit from the cloud-native paradigm. However, there are still many challenges to overcome to optimize the Quality of Service using autoscaling and resource dimensioning. Autoscaling is defined as the capacity of cloud infrastructure and applications to resize themselves by adjusting the resource pool. Simple autoscaling can be achieved by a feedback-loop based on current workload resource utilization. Called reactive autoscaling, this technique may introduce inconsistency between workload and resource utilization. In this paper, we propose a proactive autoscaling framework using a learning-based forecast model to dynamically adjust the resource pool, horizontally (number of replicas) and vertically (resources pool). This framework uses a proactive autoscaling algorithm based on Long Short-Term Memory (LSTM) to improve the end-to-end latency for cloud-native applications. We developed a proof of concept to demonstrate this framework and the effectiveness of our proactive algorithm.

Achieving network resiliency in terms of availability, reliability and fault tolerance is a central concern for network designers and operators to achieve business continuity and increase productivity. It is particularly challenging in increasingly virtualized network environments where network services are exposed to both hardware (e.g., bare-metal servers, switches, links, etc.) and software (VNF instances) failures. This increased risk of failures can severely deteriorate the quality of the deployed services and even lead to complete service outages. In this context, deploying services in operational networks often exacerbates the availability problem and requires considering availability of hardware and software components both individually and collectively. A key challenge in this perspective is the additional resources needed to achieve partial or full recovery after failures. In this paper, we propose a joint selective diversity and tailored redundancy mechanism to provision resilient services in an NFV framework. Diversity splits a single VNF into a pool of “N” active instances called replicas while redundancy provides “P” standby ready-to-use instances called backups. Based on an enhanced N+P model, we propose a placement solution of Service Function Chains (SFC) modeled as a Mixed Integer Linear Program (MILP). The proposed solution is designed to meet a target SFC availability level and, at the same time, to reduce the inherent cost due to diversity (overhead) and redundancy (backup resources). We evaluate the efficiency of the proposed solution through numerically and experimentally. Results demonstrate that our solution, not only, improves service resiliency by avoiding complete service outages but can also overcome network resource fragmentation.

The Internet of Things (IoT) market is constantly opening new business and market opportunities to connect the digital and physical worlds in many sectors such as smart-city, industry, agriculture, building, transportation, energy, etc. To support this major growth, we anticipate the adoption of mainstream Software Defined Networking SDN, Software Defined Radio (SDR) and Network Function Virtualization (NFV) technologies within the IoT systems to allow providing new ways of offering services in a more flexible, agile and cost-effective manner than today. This paper investigates the provision of NFV in the Internet of Things through the virtualization of full stack IoT functions, from radio processing functions to processing capabilities, that can be orchestrated dynamically and deployed on generic IoT devices. We provide a proof of concept to demonstrate the provisioning of end-to-end virtualized service function chain using the GNU Radio framework and dockers containers for isolation and VNF deployment and execution.

Network Function Virtualization (NFV) is a promising technology that is receiving significant attention in both academia and the industry. NFV paradigm proposes to decouple Network Functions (NFs) from dedicated hardware equipment, offering a better sharing of physical resources and providing more flexibility to network operators. However, in such environment, efficient management mechanisms are crucial to address the problem of Placement and Chaining of Virtual Network Functions (PC-VNF). In this paper, we introduce a PC-VNF model based on a flexible resource allocation approach that takes into account service requirements in terms of latency, in addition to traditional connectivity and resource utilization. This is particularly important for emerging 5G services such as ultrareliable, low latency and massive machine type communications. The end-to-end performance needs to meet the user expectations as well as service requirements to provide the desired QoS/QoE. Our main goal is to determine the optimal VNF placement minimizing resource consumption while providing specific latency (i.e., end-to-end delay) and avoiding violation of Service Level Agreements (SLA) by constraining allocated resources to a given VNF to reach its required performance. Results show that our approach achieves the required latency with better resources utilization compared to the classical approaches, with a reduction of up to 40% of resource consumption and a higher rate of accepted requests by recovering 15 to 60 % of the rejected requests.

Small-cells are rapidly emerging as the mobile operators' choice to provide additional capacity in current and future mobile networks. However, in order to fully deliver on their promises, small-cells need to address severe interference control and coordination challenges. By centralizing base-band processing in large high-volume computing infrastructures, Cloud-RAN can effectively enable advanced coordination features for dense small-cells deployments. Unfortunately, Cloud-RAN tight bandwidth and latency requirements have made optical fiber the most common solution for the links interconnecting remote radio heads (RRHs) with the base band units (BBUs), i.e. the fronthaul. Recent advances in microwave communications are making wireless fronthauls a viable option especially in dense urban environments where fiber fronthauls could be too rigid for accommodating highly dynamic traffic patterns. In this paper, we provide a novel formulation for the BBU Placement problem where BBU pools are placed at the edges of the network, possibly co-located with macro-cells, and a reconfigurable wireless fronthaul is used in order to provide RRHs with connectivity. To the best of our knowledge this is the first work to tackle the BBU placement problem over a reconfigurable substrate network with mmWave links. We also propose a BBU Placement heuristics, and we evaluate it using a numerical simulator.

Key features of satellite communications such as wide-scale coverage, broadcast/multicast support and high availability, together with significant amounts of new satellite capacity coming online, anticipate new opportunities for satellite communications services as an integral part within upcoming 5G systems. To materialize these opportunities, satellite communications services have to be provisioned and operated in a more flexible, agile and cost-effective manner than done today. In this context, this paper describes a solution for the virtualization and dynamic orchestration of satellite communication services that builds on the introduction of Software Defined Networking (SDN) and Network Function Virtualization (NFV) technologies within the satellite ground segment systems. Along with the description of the main system architecture traits, the flowchart of a general procedure for the dynamic instantiation of virtualized satellite networks on top of a SDN/NFV-enabled satellite ground segment system is provided. The paper also presents experimental results for the dynamic customization of satellite network services through the implementation of a set of virtualized satellite network functions that can be orchestrated over general purpose open virtual platforms.

Network function virtualization (NFV) sits firmly on the networking evolutionary path. By migrating network functions from dedicated devices to general purpose computing platforms, NFV can help reduce the cost to deploy and operate large IT infrastructures. In particular, NFV is expected to play a pivotal role in mobile networks where significant cost reductions can be obtained by dynamically deploying and scaling virtual network functions (VNFs) in the core network. However, in order to achieve its full potential, NFV needs to extend its reach also to the radio access segment. Here, mobile virtual network operators shall be allowed to request radio access VNFs with custom resource allocation solutions. Such a requirement raises several challenges in terms of performance isolation and resource provisioning. In this work, we formalize the wireless VNF placement problem in the radio access network as an integer linear programming problem and we propose a VNF placement heuristic, named wireless network embedding (WiNE), to solve the problem. Moreover, we present a proof-of-concept implementation of an NFV management and orchestration framework for enterprise WLANs. The proposed architecture builds on a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing capable nodes.

Abstract IEEE 802.11v and 802.11aa are two recent amendments that define new functionalities in order to support a reliable multicast transport over wireless networks. The first amendment introduces directed multicast service (DMS). On the other hand, 802.11aa defines the groupcast with retries (GCR) service, which proposes two retransmission policies: block acknowledgement (GCR‐BACK) and unsolicited retry (GCR‐UR). In this paper, we evaluate the throughput and the scalability of these new proposals using both analytical and simulation approaches. We show that DMS has the lowest scalability, while GCR‐BACK is not appropriate for groups with a large number of receivers. We conclude that GCR‐UR is the most appropriate for large groups. However, increasing the number of transmission retries reduces significantly the achieved throughput of the unsolicited retry policy. Copyright © 2016 John Wiley & Sons, Ltd.

Network Function Virtualization (NFV) is emerging as one of the most innovative concepts in the networking landscape. By migrating network functions from dedicated mid-dleboxes to general purpose computing platforms, NFV can effectively reduce the cost to deploy and to operate large networks. However, in order to achieve its full potential, NFV needs to encompass also the radio access network allowing Mobile Virtual Network Operators to deploy custom resource allocation solutions within their virtual radio nodes. Such requirement raises several challenges in terms of performance isolation and resource provisioning. In this work we formalize the Virtual Network Function (VNF) placement problem for radio access networks as an integer linear programming problem and we propose a VNF placement heuristic. Moreover, we also present a proof-of-concept implementation of an NFV management and orchestration framework for Enterprise WLANs. The proposed architecture builds upon a programmable network fabric where pure forwarding nodes are mixed with radio and packet processing nodes leveraging on general computing platforms.

Streaming over multimedia WSN (MWSN) in urban environment is challenging due to many issues among which spectrum scarcity and high radio interference. Such conditions make it difficult to ensure high bandwidth, low transmission delay and low packet losses required for real time multimedia streaming applications. In this paper, we propose COMUS a COgnitive radio solution for MUltimedia streaming over wireless Sensor networks which uses both cognitive radio technology and clustering mechanism to enhance spectrum and energy efficiency. In COMUS we consider clustering the MWSN nodes into different clusters to ensure low energy consumption. Furthermore, based on the nodes geographical position and the actual and the forecasted channel availability, we aim to ensure stable clusters forming. The multimedia streaming from a particular source node to the sink node, require a physical channel selection to perform the corresponding routing task. Thus, in COMUS we propose an efficient channel selection to prevent frequent channel switching which considers the PU (Primary User) activity forecasts. Our simulation results show that COMUS outperforms the two existing pioneering mechanisms called SEARCH and SCEEM and this in terms of providing higher video quality (PSNR and frame rate), lower end-to-end transmission delay and lower frame loss ratio under varied spectrum conditions.

Nowadays, the channel selection is a challenging task in cognitive radio because of the high radio activity and the preemptive priority of the licensed user, called primary user (PU). In this paper, we propose a DIStributed channel Selection mechanism for efficient content dissemination in COgnitive RaDio ad-hoc networks (DISCORD). DISCORD selects the most appropriate channel for content dissemination based on the PU channel occupancy and the importance of cognitive radio neighbors in the network. Indeed, a sender peer in DISCORD forecasts the channel primary user activity by the mean of the actual and statistical estimation of the channels occupancy and selects the most stable one. Moreover, it makes use of a new Social Networks Analysis (SNA) inspired metric to select the appropriate neighbors for high content dissemination in the network. The simulation results using NS2 shows that Discord presents highest performance in terms of interferences, PU priority respect and content delivery ratio in multi-hop CRNs comparing to four related protocols.

Video content delivery for vehicular ad hoc networks (VANET) under dense network conditions poses non trivial issues because of the scarcity and volatility of the wireless medium. In this context, video communication is envisioned to be of high benefit for traffic management as well as for providing value-added entertainment and advertising services. In this paper, we propose a new mechanism for efficient video streaming over VANET. The proposed mechanism selects a minimum sub-set of rebroadcaster vehicles in order to reduce interferences and achieve high video quality. The vehicles are ranked based on their strategic location in the network and their capacity to reach other vehicles using a new centrality metric inspired from the Social Network Analysis (SNA), called dissemination capacity. Through simulations, we compared our mechanism with the multichannel vehicular communication standard IEEE 1609.4 and another pioneering video streaming mechanism over VANET. The performance evaluation shows that it outperforms the abovementioned mechanisms by providing higher video delivery ratio, lower end- to-end transmission delay and lower frame loss ratio in both fully and intermittently connected networks.

This paper addresses the problem of quality bottleneck in adaptive SVC streaming. Quality bottleneck occurs in adaptive streaming systems when the desired video quality cannot be obtained even if the network capabilities are sufficient. In the context of SVC layered video streaming, we have observed that enhancement layers remain around the video source and fail to reach all the participating peers. To overcome this problem, we propose an adaptive SVC streaming solution that cooperatively integrates strategies of overlay formation, data scheduling and content adaptation. Performance evaluation using simulations shows that the proposed streaming solution reduces the quality bottleneck, increases churn-tolerance and optimizes bandwidth utilization.

In this paper, we address the problem of efficient routing in delay tolerant networks. We propose a new routing protocol dubbed as ORION. In ORION, only a single copy of a data packet is kept in the network and transmitted, contact by contact, towards the destination. The aim of the ORION routing protocol is twofold: on one hand, it enhances the delivery ratio in networks where an end-to-end path does not necessarily exist, and on the other hand, it minimizes the routing delay and the network overhead to achieve better performances. With ORION, nodes are aware of their neighborhood by the mean of actual and statistical estimation of new contacts. ORION makes use of autoregressive moving average (ARMA) stochastic processes for best contact prediction and geographical coordinates for optimal greedy data packet forwarding. Simulation results have demonstrated that ORION outperforms other existing DTN routing protocols such as PRoPHET in terms of end-to-end delay, packet delivery ratio, hop count, first packet arrival and queues occupancy.

The IEEE 802.11 standard does not define any feedback policy for the multicast transport. Hence missing multicast packets are not retransmitted and are definitely lost. In this paper we define the Block Negative-Ack (BNAK) recovery policy. Using our protocol, the multicast source transmits a block of multicast packets followed by a Block NAK Request (BNR). Upon reception of a BNR, a multicast member generates a Block NAK Response (BNAK) only if it missed some packets. A BNAK is transmitted after channel contention in order to avoid any eventual collision with other feedbacks, and is acknowledged. Under the assumption that the packet error rate (PER) of the wireless medium is low, a very limited number of BNAK will be generated and the network bandwidth will be saved. To measure the bandwidth cost of the Block NAK policy and to demonstrate its utility, we define an analytical model of our proposal and of the GCR-Block-Ack policy defined by the IEEE 802.11aa. The obtained results show that our protocol has a very high efficiency when the PER is limited, and outperforms the IEEE 802.11aa significantly.

The IEEE 802.11 standard defines the multicast transport to deliver one content to many receivers at the same time. However, no retry policy is defined for this transport mode. Therefore, missing packets are not retransmitted and are definitely lost. In IEEE 802.11 networks, transmission failures can be caused by many factors: collisions, path loss, interference, etc. Based on our measurements, packets losses caused by collisions may exceed 40% of the transmitted multicast packets. In this paper, we study the collision rate in a WLAN network and we introduce the Busy Symbol (BS) mechanism to prevent the collision of the multicast packets. The BS is perfectly compliant with the standard and requires no modifications to be deployed except at the AP. Moreover, we show that this mechanism guarantees a fair sharing of the medium between the unicast and multicast flows.

The IEEE 802.11n standard defines frame aggregation to enhance the efficiency of the legacy 802.11 MAC layer. However the new standard does not define any feedback policy for the multicast transport which remains unreliable. In this paper we present a hybrid architecture where the Long Term Evolution (LTE) network is used to enhance the reliability of the 802.11n multicast transport and the reactivity of the MAC layer for retransmitting missed MPDUs. In this architecture, the eNodeB is used to forward feedbacks to the multicast sender.

Layered streaming in peer-to-peer (P2P) networks has drawn great interests since not only it can accommodate a large number of users but also it handles heterogeneities of peers. However, there's still a lack of comprehensive study on smoothing and layer selection for layered streaming. In this work, we aim to smooth the layered stream by selecting the appropriate layers and reducing the amplitude and frequency of layer changes. We propose three objectives that should be achieved by our proposed mechanism: smoothing, minimizing useless chunk ratio and handling bandwidth fluctuation. The performance evaluation demonstrates that the proposed mechanism provides a significant improvement in the received video quality in terms of lowering the number of layer changes and useless layers chunks.

During the last decade, we witnessed a rapid growth in deployment of pull-based P2P streaming applications. In these applications, each node selects some other nodes as its neighbors and requests streaming data from them. This scheme allows eliminating data redundancy and recovering from data loss, but it pushes the complexity to the receiver node side. In this paper, we theoretically study the scheduling problem in pull-based P2P video streaming and we model it as an assignment problem. Then, we propose AsSched, new scheduling algorithm for layered streaming, in order to optimize the throughput and the delivery ratio of the system. In second time, we derive an optimal algorithm (NAsSched) for non layered streaming. The results of simulations show that our algorithms significantly outperform classic scheduling strategies especially in stern bandwidth constraints.

The IEEE 802.11v defines the Directed Multicast Service (DMS) in order to enhance the reliability of the multicast transport. This service converts multicast traffic into several unicast streams. Therefore DMS can be used to serve a limited number of receivers. In this paper, we show that using DMS, losses caused by queue rejections may exceed the packet loss rate of the legacy multicast. To resolve the limited scalability of DMS, we define a new scalable version called S-DMS. This version is appropriate for layered streams like H264/SVC and is able to avoid queue overflows. S-DMS delivers the base layer of a video traffic using DMS and transmits the enhancement layer using either DMS or the legacy multicast, depending on the available bandwidth. We show that S-DMS is able to increase considerably the number of admitted multicast members.

The peer-to-peer (P2P) networks provides a data distribution model that is attractive for Video on Demand (VoD) as it allows to decrease the costs and increase the scalability of video distribution. The two significant challenges in P2P VoD streaming are scalability and video quality. Both require efficient utilization of the resources in P2P network. Inspired by this finding, the paper addresses the problems of chunk scheduling and bandwidth allocation in P2P VoD system to efficiently utilize the upload bandwidth capacity of the peers. We first propose a queue based chunk scheduling mechanism followed by a bandwidth distribution algorithm for urgent downloading and prefetching. Our proposed mechanism allows the maximum utilization of upload bandwidth by distributing the available upload bandwidth among different queues. Experimental results show that differentiated queuing is capable of achieving the optimal streaming rate.

Although IP multicast techniques were proposed a long time ago and despite of their advantages, they are still not widely deployed due to the absence of multicast support in some routers/domains and inter-domain management issues. On the other hand, in the most of recent internet applications, where the average consumed bandwidth is measured by hundreds of Kbits per second and where the support of large-scale distribution is important, the IP multicast becomes more than a necessity. In this paper, we propose a new approach for extending the scope of IP multicast in overlay applications. We selected some overlay nodes to be used as fan-out multicast nodes and then created an IP multicast islands around each fan-out node. These multicast islands are connected with each other using unicast overlay links. This selection of fan-out nodes is based on a distributed version of K-means algorithm and GNP (Global Network Positioning), a distributed technique to measure the distance between nodes. We further propose a preventive fault tolerance mechanism for packet loss across islands. Finally, the simulation results verify the optimality of our approach in terms of link stress minimization and end-to-end delay reduction.

The massive exploitation of P2P networks and the deployment of the video streaming services is bringing significant challenges for the service providers and leading to the network efficiency problem to utilize available network resources. In this paper, we propose an ISP-driven P2P framework that enables the better interaction among the ISPs and the P2P application providers. In this framework, the interaction among different entities is facilitated by a topology-aware 2-dimensional overlay network organization driven by the ISPs that is used to organize the participating peers within the network. Furthermore, this ISP-driven overlay organization is used for the streaming service delivery of the Scalable Video Coding (SVC) contents. The proposed framework intends to ensure the improved QoS for the P2P application and enables efficient utilization of the available network resources to benefit the ISPs.

In this paper, we address the problem of efficient routing in delay tolerant network. We propose a new routing protocol dubbed as ORION. In ORION, only a single copy of a data packet is kept in the network and transmitted, contact by contact, towards the destination. The aim of the ORION routing protocol is twofold: on one hand, it enhances the delivery ratio in networks where an end-to-end path does not necessarily exist, and on the other hand, it minimizes the routing delay and the network overhead to achieve better performance. In ORION, nodes are aware of their neighbourhood by the mean of actual and statistical estimation of new contacts. ORION makes use of autoregressive moving average (ARMA) stochastic processes for best contact prediction and geographical coordinates for optimal greedy data packet forwarding. Simulation results have demonstrated that ORION outperforms other existing DTN routing protocols such as PRoPHET in terms of end-to-end delay, packet delivery ratio, hop count and first packet arrival.

This paper contributes to objective video quality assessment of broadcasted HDTV content without reference. In this context we present a new No-Reference video quality metric taking into account the behavior of Human Visual System. This new metric called WMBER is based on macro-blocks error detection weighted by saliency maps computed at the decoder side. Moreover, both macro-block error detection and saliency maps processing require only partial decoding allowing real-time performance. A subjective experiment has been carried out to evaluate the performances of the proposed metric. The results are compared to the Full Reference metric MSE. The evaluation of the results shows that the proposed method provides a very good prediction of subjective measures.

One of the major issues experienced by the IEEE 802.11 standard is the fair sharing of the channel between unicast and multicast flows. This problem is obviously caused by the use of the smallest Contention Window size to transmit the multicast packets compared to an exponentially incremented size for every unicast packet transmission retry. This pattern is due mainly to the reliability degree difference between the unicast and the multicast traffic. Currently, the unicast uses feedbacks to provide reliability, whereas the multicast does not rely on such a mechanism and is therefore considered as unreliable. In this paper we propose a reliable Multicast flow transport proposal called the Reliable PLCP-based Multicast Protocol (RPMP) and we study its impact on the fair channel sharing with unicast flows in high-load conditions. We show that RPMP solves the issues of the legacy multicast.

In this paper we propose SWOR, an architecture to deliver Scalable Video Coding (SVC) contents over P2P network. SWOR architecture is based on twofold mechanisms: (1) organization of peers in Small-World (SW) overlay networks, (2) delivering SVC content using push-pull mechanism. SWOR is evaluated and compared with CoolStreaming/DONet (CS) on QoS metrics using NS-2 simulator. Performance evaluation demonstrates that SWOR outperforms CS and provides a significant improvement in the received video quality in terms of lowering the packet loss and transmission delay.

This paper presents an Adaptive Greedy-compass Energy-aware Multipath protocol (AGEM), a novel routing protocol for wireless multimedia sensors networks (WMSNs). AGEM uses sensors node positions to make packet forwarding decisions. These decisions are made online, at each forwarding node, in such a way that there is no need for global network topology knowledge and maintenance. AGEM routing protocol performs load-balancing to minimize energy consumption among nodes using twofold policy: (1) smart greedy forwarding, based on adaptive compass and (2) walking back forwarding to avoid holes. Performance evaluations of AGEM compared to GPSR (Greedy Perimeter Stateless Routing) show that AGEM can: (a) maximize the network lifetime, (b) guarantee quality of service for video stream transmission, and (c) scale better on densely deployed wireless sensors network.

The wide adoption of the P2P networks for the deployment of IPTV services at large scale seems very fascinating. However, it has started exposing the other side of canvas that is not acceptable for the provisioning of QoS for P2P applications. It is even more frustrating for the ISPs due to inefficient utilization of network resources. In this paper, we investigate the conflicting objectives of P2P applications & ISPs and we propose a framework enabling better interaction among entities involved in the IPTV service delivery mechanism. This interaction is facilitated by a topology-aware overlay network organization driven by the ISPs that is used to organize the participating peers within the network. The proposed ISPs-driven overlay organization facilitates the multi-attribute based sender peers selection mechanism and thus ensures the efficient utilization of available resources in ISPs domain and guarantees better QoS for the P2P applications. The framework is evaluated using the networks simulations to conform its viability. The obtained results show a noticeable improvement in the received QoS for the P2P applications and enable the efficient utilization of the available network resources to benefit the ISPs.

In this paper, we propose a cross layer optimizer named XLO between scalable video streaming application and IEEE 802.16 MAC layer. The main objective is to allow video streaming application to adapt its parameters according to 802.16 MAC layer conditions and resource availability. XLO uses the existing service flow management messages exchanged between base station (BS) and subscriber station (SS) and makes them available to the video streaming application via a specific XLO interface. We implemented the XLO in the QualNet simulator and performed extensive simulations using a personalized scalable video traffic generator, capable of streaming video with different data rates. We also introduce an enhanced admission control function at the BS that takes into account video adaptability property. The simulation results show the effectiveness of our XLO mechanism for delivering better quality of service.

In order to provide the user with the best quality of experience, handling network heterogeneity is essential. In this paper, we investigate the design of a cross layer architecture for scalable video streaming applications in the context of heterogeneous broadband networks. We use the data rate adaptation feature in the video streaming application to meet the destination MAC/physical layer conditions and resource availability. Resources include the end-to-end available bandwidth between the source video server and customers from heterogeneous networks including WiMax, WiFi, 3G, and wired networks. We also introduce an enhanced admission control function at the destination gateway (to which the destination is attached) that takes into account video adaptability property. In order to illustrate the effectiveness of our mechanism, we implemented our solution named CLO (Cross Layer Optimizer) in the QualNet simulator over a WiMAX network. We utilize the existing MAC management messages exchanged between base and subscriber stations (BS and SS) and provide them to the video streaming application via a specific CLO interface. In order to assess the performance of CLO, we conducted extensive simulations using a personalized scalable video traffic generator, capable of providing different video data rates. The simulation results confirm remarkable improvement in the quality perceived by the user.

In order to improve scalability and reduce maintenance overhead for Peer-to-Peer system, several architecture with constant degree and optimal diameter are proposed. However, the expected topology doesn't effectively utilize the bandwidth capacity of peers. In this work, we propose K-overlay, an overlay scheme based on unbalanced Kautz graph with logdndiameter and constant in-degree. We define the degree of a digraph as the maximum number of arcs arriving at or leaving from any vertex. The diameter of a graph is the number of arc traversals that is sufficient to reach any vertex from any other vertex. K-overlay structure is based on two-fold mechanism. (1) Organization of peers in concentric circles to maximize the outgoing bandwidth of peers (2) Content delivery through parent as well as neighboring peers. Through formal analysis and comprehensive simulations, we show that our proposed architecture achieves optimal diameter and good connectivity as compared to existing overlay architecture like P2Cast.

In this paper, we present an admission control mechanism for video streaming driven by service provider (SP) over P2P networks. SP-driven P2P network is a network in which service provider has a comprehensive control over contracted resources utilisation. SP assures the coordination and the creation of overlay P2P network and it is responsible for admission control and resource allocation among others. Our proposed quality-of-service (QoS) provisioning mechanism employs video traffic descriptor (i.e. traffic specification) for performing admission control. The proposed mechanism is evaluated using ns2 simulator and the results show the significance of admission control mechanism for QoS provisioning.

P2P-based IPTV service delivery is considered as the next killer application over the Internet. P2P systems address the issues of scalability and are considered most suitable architecture for the real-time content distribution/sharing. On the other hand, P2P applications show greedy behavior in the consumption of available network bandwidth and leads to the problems of inefficient utilization of network resources and poor interactions with other applications. These problems are due to the lack of coordination among service providers (SPs) and P2P applications. Thus, in this paper, we present a SP-driven P2P framework for the IPTV service delivery to facilitate the service delivery mechanism with improved Quality of Services (QoS). The proposed mechanism is assisted by the explicit communications among SPs and P2P applications for efficient utilization of available resources. The proposed mechanism also addresses the issues related to QoS provisioning and the nonlinear service distribution.

Enabling end-to-end quality of service (QoS) for realtime video content delivery across heterogeneous P2P networks is challenging but vital for the efficient service provision. In this paper, we present a P2P based admission control mechanism for real-time video streaming driven by service provider (SP). SP- driven P2P network is a network where service provider has a comprehensive control over contracted resource utilization. SP assures the coordination and the creation of overlay P2P network and it is responsible for admission control and resource allocation among others. Our proposed QoS provision mechanism employs video traffic descriptor (i.e. traffic specification) for performing admission control. Before accepting a particular P2P video streaming session, the SP controls the access to the network resources by performing policy and admission control. After retrieving the service availability from its traffic repository, SP decides whether to accept or to reject the request. The proposed SP-driven P2P admission control mechanism is evaluated using ns2 simulator and the results show that our proposed mechanism allows the provisioning of end-to-end QoS for delivered video content over P2P network.

Today, the IPTV technology is emerging as a new concept for delivering and accessing audiovisual services over IP networks. To allow the transmission of IPTV services on wired and wireless networks, different issues should be overcome: scalability, QoS provisioning, security guaranty, and terminals heterogeneity. In this regards, there is a relation between QoS and security as both need to be carefully and tightly managed in a complete framework and not tackled separately. In this paper, we show how QoS and security are tightly managed in a complete end-to-end architecture for providing IPTV services while addressing the majority of the above issues (scalability, QoS, security, and heterogeneity). In particular, we propose an IPTV architecture that deals with end-to-end QoS and security management at two segments: the core network and the access network. The core network is composed of independent domains where each of them is managed by a local domain manager having service level negotiation capabilities. The access network is considered as WLAN (802.11) which connects heterogeneous mobile terminals. The proposed IPTV architecture includes two main entities: the Content Server which provides the TV stream in the core network and the Adaptation Gateway that assures the TV stream adaptation in wireless network according to terminal capabilities and user profile.

Thanks to its cost-effectiveness and wide deployment, the 802.11 WLAN is considered today as the de-facto wireless access network for the last mile connection. In this regards, QoS provisioning in 802.11 WLAN for multimedia services is becoming a main objective that should be enforced to enable reliable commercial offers. To reach this goal, the different mechanisms proposed by 802.11 standards, including fragmentation, physical rate variation, retransmission, and transmission power adjustment, should be exploited intelligently by the application using a cross-layer interaction. This would allow service scalability by improving resources availability and enhancing QoS provisioning. In this paper, we propose an adaptive cross layer fragmentation technique aiming to provide a reliable and QoS-based IPTV service delivery over 802.11 WLAN. In our architecture, the link layer fragmentation is controlled automatically by the application layer in a cross-layer interaction to find a trade-off between the video quality degradation and the overhead ratio introduced by the MAC fragmentation. Performance evaluation conducted using an experimental test-bed shows the ability of our cross-layer mechanism to reduce packets video losses and to enhance the overall video objective quality given by the PSNR and the SSIM metrics.

In the last few years, we have witnessed a rapid growth of multimedia content delivery across the networks. Peer-to-Peer (P2P) networks are considered as one of the most suitable targeted infrastructure for supporting real time streaming and has played vital role in this growth. IPTV service is considered as the emerging application that has a great potential to incur new revenues for contents and service providers. This service gives more choice to the end users to consume TV programs in their personalized way and beyond any geographical constraints. The exploitation of P2P- based IPTV services has turned the dream of Universal Media Access (UMA) in reality. However, the efficient deployment of P2P-based IPTV with better Quality of Service (QoS) needs to address certain challenges. However, for the provision of better QoS IPTV service, we need to cope with the dynamic characteristics of P2P framework that can deter drastically the performance of such applications. In this paper, we emphasize some challenges, opportunities, and open issues regarding IPTV service over mobile terminals operating over P2P based networks.

This paper presents an approach to the management and provisioning of end-to-end quality of service (QoS) in next generation networks (IP and non-IP networks) that is being developed in the IST ENTHRONE project. We present an overview of the multimedia caching and its integration with different approaches for the efficient contents delivery across heterogeneous networks. In particular, we enlighten edge and data casting techniques with proper deployment at router/gateway and/or terminals in content delivery networks architecture.

In this paper, we leverage the characteristics of scalable video coding (SVC) and P2P networks in order to propose an efficient and adaptive video streaming mechanism. We aim to maintain smooth content delivery of certain acceptable QoS (quality of service) for the received video. The proposed system is composed of an adaptive streaming mechanism which is based on the efficient SVC content scheduling to coordinate among receiver and sender peers for smooth media streaming. Performance evaluation of our system conducted using NS-2 simulation demonstrates a significant improvement in the received video quality compared to conventional streaming approaches.

Next generation wireless systems are characterised by IP-based infrastructure with heterogeneous access technologies which complements each other in terms of data-rate and coverage. Users in this context are equipped with multi-functional mobile devices, enabling access to different networks. The proliferation of these devices ranging from mobile communication to home electronics and their inter-working forms a ubiquitous environment around the user. The personal ubiquitous environments (PUE) concept allows the user to have ubiquitous global access to a vast number and variety of information resources without dwelling into the diversity of devices and networks. In this paper, we present the PUE mobility management (MM) architecture that enables global seamless roaming in All-IP heterogeneous wireless networks. The PUE mobility management architecture is further evaluated (with the help of simulations) under a variety of everyday usage scenarios and the proof-of-concept is presented. Our simulation results show that the PUE MM architecture efficiently manages the resources available in the userpsilas PUE and offers seamless handover experience in always changing, heterogeneous all-IP wireless networks.

An ever-growing demand for coverage extension, higher data-rates and improved connectivity has motivated the interconnection of mobile ad-hoc networks to fixed IP networks, to form a Hybrid Wireless Network (HWN). The convergence of ad- hoc and infrastructure network is also attractive in real-world scenarios due to its practicality and usefulness. In HWNs, gateways are generally used as an interconnection point for the integrated network. In order to improve the overall performance of HWN, an efficient gateway discovery and route maintenance scheme is indispensable. In this paper, we present a novel adaptive gateway discovery scheme called ADD (Adaptive Distributed gateway Discovery), in which the adaptation is done in a fully distributed manner. Simulation results show that the distributed adaptation capabilities of our proposed scheme facilitate outperforming the existing gateway discovery mechanisms in terms of control overhead, packet delivery and delay.

This paper describes a new architecture and implementation of an adaptive streaming system (e.g., television over IP, video on demand) based on cross-layer interactions. At the center of the proposed architecture is the meet in the middle concept involving both bottom-up and top-down cross layer interactions. Each streaming session is entirely controlled at the RTP layer where we maintain a rich context that centralizes the collection of (i) instantaneous network conditions measured at the underlying layers (i.e.: link, network, and transport layers) and (ii) user- and terminal-triggered events that impose new real-time QoS adaptation strategies. Thus, each active multimedia session is tied to a broad range of parameters, which enable it to coordinate the QoS adaptation throughout the protocol layers and thus eliminating the overhead and preventing counter-productiveness among separate mechanisms implemented at different layers. The MPEG-21 framework is used to provide a common support for implementing and managing the end-to-end QoS of audio/video streams. Performance evaluations using peak signal to noise ratio (PSNR) and structural similarity index (SSIM) objective video quality metrics show the benefits of using the proposed Meet In the Middle cross-layer design compared to traditional media delivery approaches.

The proliferation of heterogeneous devices, ranging from mobile communication to home electronics and their internetworking have resulted into the development of a "personal ubiquitous environment" around the user. In this increasingly complex world, personal networks (PN) and personal network federations (PN-F) provide simple but powerful concepts towards the vision of "personal ubiquitous environments". This paper proposes solutions to realise the concepts of PN and PN-F. To this end, we first present the integrated convergence (ICON) layer which offers seamless interoperability across heterogeneous access technologies at mobile nodes in PNs. Then, we introduce the personal network routing protocol (PNRP) for personal ubiquitous environments. We also provide the implementation and the evaluation of PNRP. Our results show that PNRP efficiently manages the user's personal environments i.e. PNs and PN-Fs. Moreover, in terms of delay and packet delivery fraction, it performs almost similar to the traditional routing protocols such as OLSR and AODV. In contrast, PNRP consistently generates less routing load than any flat proactive MANET routing protocol.

This paper describes the issues related to IP multicast support in satellites networks. The IGMP multicast membership management protocol is considered as inappropriate for multicast group management over satellite network. In fact, IGMP leads to flooding, latency and scalability issues when used over asymmetric satellite networks. Thus, we propose a new architecture for multicast services provisioning based on IP multimedia subsystem (IMS) as a service delivery platform. The interworking between IP multicast protocol (i.e., IGMP) and IMS protocol (i.e., SIP) is demonstrated to overcome these multicast scalability issues inherent to satellite networks. Implementation and deployment of the proposed platform over an advanced satellite network emulator is also described.

The proliferation of heterogeneous devices, ranging from mobile communication to home electronics and their internetworking have resulted into the development of a "personal ubiquitous environment" around the user. In this increasingly complex world, personal networks (PN) and personal network federations (PN-F) provide simple but powerful concepts towards the vision of "personal ubiquitous environments". In this paper, we present the profile-based network layer design to realise the federations of personal networks. To this end, we introduce the possible architectures for PN federations and their initiation, formation and usage procedures, which are assisted by personal network profiles. Moreover, we also discuss the PN cluster formation procedure, which is performed by personal network routing protocol (PNRP). For the implementation and the evaluation of our proposed solutions, we particularly focus on spontaneous PN-F architecture. The proof-of-concept is given and the performance of our spontaneous PN-F scheme is evaluated.

The actual trend in the IT (information technology) market is to create a new environment that will enable end-users to access a variety of services (TV/Radio programs, Internet, Multimedia applications, Games ...), wherever they are, whenever they want and whatever terminal they use. No existing solution exists that allows the common user to have access, instantly and in real time, to his desired range of services and applications no matter when and where he may geographically be. With the emergence of Beyond-3G/4G systems and environments, network access possibilities will be much more present throughout the world. Based on this assumption, we present an approach that would permit to provide users anywhere around the globe with their desired services, even where the service is not accessible. Through the use of an ambient middleware, with MPEG-21 and metadata-based functionalities, the requested service will also be efficiently adapted for the benefit of the user.

In this paper we consider the problem of real-time streaming of IP packet video over Peer-to-Peer networks (P2P) from multiple senders to a single receiver. P2P networks are characterized by a potentially large and highly dynamic population of hosts that join and leave the network frequently. We present the design and evaluation of a quality adaptation streaming mechanism in a multi-source streaming to a single receiver. Multimedia streaming is a real time application so, the main challenges in the design of this mechanism are (1) selection of senders peers nodes; (2) stream switching among the peers; (3) optimizing video quality by active measurements of links; and (4) enhancing the overall Quality of Service (QoS). Our key technique to provide quality adaptation is based on active measurements of network links and selection of sender peers to enhance the overall throughput. We used video traffic organized as MDC (Multiple Description Coding) layers, which provides high error resilient. Our simulations results using ns2 show that our solution allows to efficiently utilize available network bandwidth of sending peers and allow maximizing streaming qualities at the reception peer.

Audio and video streaming over the Internet is becoming very attractive and popular. Thus, there is a need to provide efficient delivery of such applications to the growing number of demanding users. In addition, new types of devices, transportable and mobile are emerging rapidly. Now, common users are expecting to access their favorite Internet services anywhere, and through any equipment (laptop, PDA, ...). This paper combines those two requests by proposing an efficient adaptive solution to mobile users willing to receive audio-video streaming services. Our solution is focused on MPEG-4 coded streams, the use of audio-visual objects (AVOs) and the partitioning into different sets of layers. According to the changes of networks experienced by the mobile user, a classification followed by an adaptation process is performed to the video stream. AVOs are added or dropped according to the newly accessed network constraints and following a TCP-friendly rate control algorithm

There is an increasing demand for supporting real-time audiovisual services over next-generation wired and wireless networks. Various link/network characteristics make the deployment of such demanding services more challenging than traditional data applications like e-mail and the Web. These audiovisual applications are bandwidth adaptive but have stringent delay, jitter, and packet loss requirements. Consequently, one of the major requirements for the successful and wide deployment of such services is the efficient transmission of sensitive content (audio, video, image) over a broad range of bandwidth-constrained access networks. These media will be typically compressed according to the emerging ISO/IEC MPEG-4 standard to achieve high bandwidth efficiency and content-based interactivity. MPEG-4 provides an integrated object-oriented representation and coding of natural and synthetic audiovisual content for its manipulation and transport over a broad range of communication infrastructures. In This work, we leverage the characteristics of MPEG-4 and Internet protocol (IP) differentiated service frameworks, to propose an innovative cross-layer content delivery architecture that is capable of receiving information from the network and adaptively tune transport parameters, bit rates, and QoS mechanisms according to the underlying network conditions. This service-aware IP transport architecture is composed of: 1) an automatic content-level audiovisual object classification model; 2) a reliable application level framing protocol with fine-grained TCP-Friendly rate control and adaptive unequal error protection; and 3) a service-level QoS matching/packet tagging algorithm for seamless IP differentiated service delivery. The obtained results demonstrate, that breaking the OSI protocol layer isolation paradigm and injecting content-level semantic and service-level requirements within the transport and traffic control protocols, lead to intelligent and efficient support of multimedia services over complex network architectures.

Robust video streaming over wireless local area networks (IEEE 802.11) faces many challenges, including bandwidth channel variations, data errors/losses, and terminal capacity heterogeneity. This is worsen by the TCP/IP architecture that does not offer any quality of service QoS guarantees to demanding applications such as video streaming. In order to improve error resilience and user-perceived video quality, a novel error control protocol, called "unequally interleaved forward error correction" (UI-FEC), is proposed. UI-FEC is particularly efficient for adaptive MPEG-4 video multicast over wireless LAN. The proposed protocol is composed of (1) a coordinated unequal and interleaved MPEG-4 data protection mechanism, that gracefully degrades video quality at receivers while minimizing the overall link bandwidth consumption; (2) an adaptive MPEG-4 video fragmentation and encapsulation protocol for a higher wireless link utilization.

This article describes a fair and robust video streaming framework over IP networks. It is based on an MPEG-4 audio-visual object (AVOs) classification, TCP-friendly transport and out-of-band unequal forward error protection. According to network congestion feedback, video source servers dynamically adjust their bit rates by adding and dropping MPEG-4 AVO to conform to the TCP-friendly rate control (TFRC) algorithm and by taking into consideration media semantic relevancy. Thus, an accurate MPEG-4 access unit (AU) partitioning and packetization can be performed to cope with decoding error propagation and network bandwidth fluctuation. Finally, AVOs requiring similar network QoS level are automatically classified, packetized and mapped to one of the available EP DiffServ PHB (per hop behaviors). Simulation results show a significant improvement regarding to user-perceived video quality, packet loss recovery and bandwidth share fairness.

In this article, we investigate efficient transmission of object-based MPEG-4 video over IP networks with QoS management capabilities. MPEG-4 audio visual objects (AVOs) are classified based on application-level QoS criteria and AVOs semantic descriptors according to MPEG-7 framework MPEG-4 AVOs requiring same QoS performance form the network are automatically classified and multiplexed within one of the IP DiffServ PHB (per hop behaviours). Object data-packets within the same class are then transmitted over the selected transport layer with the corresponding bearer capability and priority level. We propose to extend the MPEG-4 system architecture with a new "media QoS classification layer". This layer provides automatic and accurate mapping between MPEG-4 application-level QoS metrics and underlying transport network with QoS mechanisms such as IP DiffServ. The "media QoS classification layer" makes use of a neural network classification model that is transparent to application and network layers. Implementation and performance evaluation of the proposal are also described.

This article discusses technical issues related to delivery and control of IP multimedia services, such as videoconferencing, involving heterogeneous end terminals. In particular, it describes the design and implementation of an experimental system for interworking between IETF SIP (session initiation protocol) and ISO MPEG-4 DMIF (delivery multimedia integration framework) session and call control signaling protocols. This IP videoconferencing interworking system is composed of two core units for supporting delivery of audio-video streams from a DMIF domain to a SIP domain (i.e. DMIF2SIP unit) and from a SIP domain to a DMIF domain (i.e. SIP2DMIF unit). These units perform various translation functions for transparent establishment and control of multimedia sessions across an IP networking environment, including, session protocol conversion, service gateway conversion and address translation.

In this article an implementation of a JAVA-based MPEG-4 video on demand service over IP with DMIF signaling is proposed. The focus is on performance evaluation on transmitting video streams over IP Differentiated Services with Linux-based IP DiffServ gateways and real MPEG video sequences. Our contribution is to demonstrate that IP DiffServ's Assured Forwarding PHB is a serious candidate for supporting real-time video communications in association with intelligent packet marking and scheduling mechanisms.

Traditional IP networks offer users best-effort service. In this model, all user packets compete equally for network resources. Much attention is being given to developing IP QoS (quality of service), which allows network operators to offer differing levels of treatment to user packets. In this article, we investigate QoS interaction provisioning between MPEG-4 video applications and IP DiffServ networks. QoS interaction is performed through intelligent encapsulation and marking of MPEG-4 elementary streams (video, audio, signaling, scenes and object descriptors) on IP Diffserv's AF/EF per hop behavior. Our contribution to this issue is twofold: first, we analyze the different proposals for MPEG-4 encapsulation protocols over RTP/IP; and second, we propose and evaluate a video-oriented marking mechanism for IP Diffserv edge routers. A performance evaluation is carried through simulation using ns2.