Algorithms for advance bandwidth reservation in media production networks

Media production generally requires many geographically distributed actors (e.g., production houses, broadcasters, advertisers) to exchange huge amounts of raw video and audio data. Traditional distribution techniques, such as dedicated point-to-point optical links, are highly inefficient in terms of installation time and cost. To improve efficiency, shared media production networks that connect all involved actors over a large geographical area, are currently being deployed. The traffic in such networks is often predictable, as the timing and bandwidth requirements of data transfers are generally known hours or even days in advance. As such, the use of advance bandwidth reservation (AR) can greatly increase resource utilization and cost efficiency. In this paper, we propose an Integer Linear Programming formulation of the bandwidth scheduling problem, which takes into account the specific characteristics of media production networks, is presented. Two novel optimization algorithms based on this model are thoroughly evaluated and compared by means of in-depth simulation results

A signaling architecture for multimedia MBS over WiMAX

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Design of an adaptive RF fingerprint indoor positioning system

RF fingerprinting can solve the indoor positioning problem with satisfactory accuracy, but the methodology depends on the so-called radio map calibrated in the offline phase via manual site-survey, which is costly, time-consuming and somewhat error-prone. It also assumes the RF fingerprint’s signal-spatial correlations to remain static throughout the online positioning phase, which generally does not hold in practice. This is because indoor environments constantly experience dynamic changes, causing the radio signal strengths to fluctuate over time, which weakens the signal-spatial correlations of the RF fingerprints. State-of-the-arts have proposed adaptive RF fingerprint methodology capable of calibrating the radio map in real-time and on-demand to address these drawbacks. However, existing implementations are highly server-centric, which is less robust, does not scale well, and not privacy-friendly. This thesis aims to address these drawbacks by exploring the feasibility of implementing an adaptive RF fingerprint indoor positioning system in a distributed and client-centric architecture using only commodity Wi-Fi hardware, so it can seamlessly integrate with existing Wi-Fi network and allow it to offer both networking and positioning services. Such approach has not been explored in previous works, which forms the basis of this thesis’ main contribution. The proposed methodology utilizes a network of distributed location beacons as its reference infrastructure; hence the system is more robust since it does not have any single point-of-failure. Each location beacon periodically broadcasts its coordinate to announce its presence in the area, plus coefficients that model its real-time RSS distribution around the transmitting antenna. These coefficients are constantly self-calibrated by the location beacon using empirical RSS measurements obtained from neighbouring location beacons in a collaborative fashion, and fitting the values using path loss with log-normal shadowing model as a function of inter-beacon distances while minimizing the error in a least-squared sense. By self-modelling its RSS distribution in real-time, the location beacon becomes aware of its dynamically fluctuating signal levels caused by physical, environmental and temporal characteristics of the indoor environment. The implementation of this self-modelling feature on commodity Wi-Fi hardware is another original contribution of this thesis. Location discovery is managed locally by the clients, which means the proposed system can support unlimited number of client devices simultaneously while also protect user’s privacy because no information is shared with external parties. It starts by listening for beacon frames broadcasted by nearby location beacons and measuring their RSS values to establish the RF fingerprint of the unknown point. Next, it simulates the reference RF fingerprints of predetermined points inside the target area, effectively calibrating the site’s radio map, by computing the RSS values of all detected location beacons using their respective coordinates and path loss coefficients embedded inside the received beacon frames. Note that the coefficients model the real-time RSS distribution of each location beacon around its transmitting antenna; hence, the radio map is able to adapt itself to the dynamic fluctuations of the radio signal to maintain its signal-spatial correlations. The final step is to search the radio map to find the reference RF fingerprint that most closely resembles the unknown sample, where its coordinate is returned as the location result. One positioning approach would be to first construct a full radio map by computing the RSS of all detected location beacons at all predetermined calibration points, then followed by an exhaustive search over all reference RF fingerprints to find the best match. Generally, RF fingerprint algorithm performs better with higher number of calibration points per unit area since more locations can be classified, while extra RSS components can help to better distinguish between nearby calibration points. However, to calibrate and search many RF fingerprints will incur substantial computing costs, which is unsuitable for power and resource limited client devices. To address this challenge, this thesis introduces a novel algorithm suitable for client-centric positioning as another contribution. Given an unknown RF fingerprint to solve for location, the proposed algorithm first sorts the RSS in descending order. It then iterates over this list, first selecting the location beacon with the strongest RSS because this implies the unknown location is closest to the said location beacon. Next, it computes the beacon’s RSS using its path loss coefficients and coordinate information one calibration point at a time while simultaneously compares the result with the measured value. If they are similar, the algorithm keeps this location for subsequent processing; else it is removed because distant points relative to the unknown location would exhibit vastly different RSS values due to the different site-specific obstructions encountered by the radio signal propagation. The algorithm repeats the process by selecting the next strongest location beacon, but this time it only computes its RSS for those points identified in the previous iteration. After the last iteration completes, the average coordinate of remaining calibration points is returned as the location result. Matlab simulation shows the proposed algorithm only takes about half of the time to produce a location estimate with similar positioning accuracy compared to conventional algorithm that does a full radio map calibration and exhaustive RF fingerprint search. As part of the thesis’ contribution, a prototype of the proposed indoor positioning system is developed using only commodity Wi-Fi hardware and open-source software to evaluate its usability in real-world settings and to demonstrate possible implementation on existing Wi-Fi installations. Experimental results verify the proposed system yields consistent positioning accuracy, even in highly dynamic indoor environments and changing location beacon topologies

Role of Wireless technology in mobile augmented reality system (MARS)

Not availabl

EVEREST IST - 2002 - 00185 : D23 : final report

Deliverable públic del projecte europeu EVERESTThis deliverable constitutes the final report of the project IST-2002-001858 EVEREST. After its successful completion, the project presents this document that firstly summarizes the context, goal and the approach objective of the project. Then it presents a concise summary of the major goals and results, as well as highlights the most valuable lessons derived form the project work. A list of deliverables and publications is included in the annex.Postprint (published version

Architectures and technologies for quality of service provisioning in next generation networks

A NGN is a telecommunication network that differs from classical dedicated networks because of its capability to provide voice, video, data and cellular services on the same infrastructure (Quadruple-Play). The ITU-T standardization body has defined the NGN architecture in three different and well-defined strata: the transport stratum which takes care of maintaining end-to-end connectivity, the service stratum that is responsible for enabling the creation and the delivery of services, and finally the application stratum where applications can be created and executed. The most important separation in this architecture is relative to transport and service stratum. The aim is to enable the flexibility to add, maintain and remove services without any impact on the transport layer; to enable the flexibility to add, maintain and remove transport technologies without any impact on the access to service, application, content and information; and finally the efficient cohesistence of multiple terminals, access technologies and core transport technologies. The Service Oriented Architecture (SOA) is a paradigm often used in systems deployment and integration for organizing and utilizing distributed capabilities under the control of different ownership domains. In this thesis, the SOA technologies in network architetures are surveyed following the NGN functional architecture as defined by the ITU-T. Within each stratum, the main logical functions that have been the subject of investigation according to a service-oriented approach have been highlighted. Moreover, a new definition of the NGN transport stratum functionalities according to the SOA paradigm is proposed; an implementation of the relevant services interfaces to analyze this approach with experimental results shows some insight on the potentialities of the proposed strategy. Within NGN architectures research topic, especially in IP-based network architectures, Traffic Engineering (TE) is referred to as a set of policies and algorithms aimed at balancing network traffic load so as to improve network resource utilization and guarantee the service specific end-to-end QoS. DS-TE technology extends TE functionalities to a per-class basis implementation by introducing a higher level of traffic classification which associates to each class type (CT) a constraint on bandwidth utilization. These constraints are set by defining and configuring a bandwidth constraint (BC) model whih drives resource utilization aiming to higher load balancing, higher QoS performance and lower call blocking rate. Default TE implementations relies on a centralized approach to bandwidth and routing management, that require external management entities which periodically collect network status information and provide management actions. However, due to increasing network complexity, it is desiderable that nodes automatically discover their environment, self-configure and update to adapt to changes. In this thesis the bandwidth management problem is approached adopting an autonomic and distributed approach. Each node has a self-management module, which monitors the unreserved bandwidth in adjacent nodes and adjusts the local bandwidth constraints so as to reduce the differences in the unreserved bandwidth of neighbor nodes. With this distributed and autonomic algorithm, BC are dinamically modified to drive routing decision toward the traffic balancing respecting the QoS constraints for each class-type traffic requests. Finally, Video on Demand (VoD) is a service that provides a video whenever the customer requests it. Realizing a VoD system by means of the Internet network requires architectures tailored to video features such as guaranteed bandwidths and constrained transmission delays: these are hard to be provided in the traditional Internet architecture that is not designed to provide an adequate quality of service (QoS) and quality of experience (QoE) to the final user. Typical VoD solutions can be grouped in four categories: centralized, proxy-based, Content Delivery Network(CDN) and Hybrid architectures. Hybrid architectures combine the employment of a centralized server with that of a Peer-to-peer (P2P) network. This approach can effectively reduce the server load and avoid network congestions close to the server site because the peers support the delivery of the video to other peers using a cache-and-relay strategy making use of their upload bandwidth. Anyway, in a peer-to-peer network each peer is free to join and leave the network without notice, bringing to the phenomena of peer churns. These dynamics are dangerous for VoD architectures, affecting the integrity and retainability of the service. In this thesis, a study aimed to evaluate the impact of the peer churn on the system performance is proposed. Starting from important relationships between system parameters such as playback buffer length, peer request rate, peer average lifetime and server upload rate, four different analytic models are proposed

Architectures and technologies for quality of service provisioning in next generation networks

A NGN is a telecommunication network that differs from classical dedicated networks because of its capability to provide voice, video, data and cellular services on the same infrastructure (Quadruple-Play). The ITU-T standardization body has defined the NGN architecture in three different and well-defined strata: the transport stratum which takes care of maintaining end-to-end connectivity, the service stratum that is responsible for enabling the creation and the delivery of services, and finally the application stratum where applications can be created and executed. The most important separation in this architecture is relative to transport and service stratum. The aim is to enable the flexibility to add, maintain and remove services without any impact on the transport layer; to enable the flexibility to add, maintain and remove transport technologies without any impact on the access to service, application, content and information; and finally the efficient cohesistence of multiple terminals, access technologies and core transport technologies. The Service Oriented Architecture (SOA) is a paradigm often used in systems deployment and integration for organizing and utilizing distributed capabilities under the control of different ownership domains. In this thesis, the SOA technologies in network architetures are surveyed following the NGN functional architecture as defined by the ITU-T. Within each stratum, the main logical functions that have been the subject of investigation according to a service-oriented approach have been highlighted. Moreover, a new definition of the NGN transport stratum functionalities according to the SOA paradigm is proposed; an implementation of the relevant services interfaces to analyze this approach with experimental results shows some insight on the potentialities of the proposed strategy. Within NGN architectures research topic, especially in IP-based network architectures, Traffic Engineering (TE) is referred to as a set of policies and algorithms aimed at balancing network traffic load so as to improve network resource utilization and guarantee the service specific end-to-end QoS. DS-TE technology extends TE functionalities to a per-class basis implementation by introducing a higher level of traffic classification which associates to each class type (CT) a constraint on bandwidth utilization. These constraints are set by defining and configuring a bandwidth constraint (BC) model whih drives resource utilization aiming to higher load balancing, higher QoS performance and lower call blocking rate. Default TE implementations relies on a centralized approach to bandwidth and routing management, that require external management entities which periodically collect network status information and provide management actions. However, due to increasing network complexity, it is desiderable that nodes automatically discover their environment, self-configure and update to adapt to changes. In this thesis the bandwidth management problem is approached adopting an autonomic and distributed approach. Each node has a self-management module, which monitors the unreserved bandwidth in adjacent nodes and adjusts the local bandwidth constraints so as to reduce the differences in the unreserved bandwidth of neighbor nodes. With this distributed and autonomic algorithm, BC are dinamically modified to drive routing decision toward the traffic balancing respecting the QoS constraints for each class-type traffic requests. Finally, Video on Demand (VoD) is a service that provides a video whenever the customer requests it. Realizing a VoD system by means of the Internet network requires architectures tailored to video features such as guaranteed bandwidths and constrained transmission delays: these are hard to be provided in the traditional Internet architecture that is not designed to provide an adequate quality of service (QoS) and quality of experience (QoE) to the final user. Typical VoD solutions can be grouped in four categories: centralized, proxy-based, Content Delivery Network(CDN) and Hybrid architectures. Hybrid architectures combine the employment of a centralized server with that of a Peer-to-peer (P2P) network. This approach can effectively reduce the server load and avoid network congestions close to the server site because the peers support the delivery of the video to other peers using a cache-and-relay strategy making use of their upload bandwidth. Anyway, in a peer-to-peer network each peer is free to join and leave the network without notice, bringing to the phenomena of peer churns. These dynamics are dangerous for VoD architectures, affecting the integrity and retainability of the service. In this thesis, a study aimed to evaluate the impact of the peer churn on the system performance is proposed. Starting from important relationships between system parameters such as playback buffer length, peer request rate, peer average lifetime and server upload rate, four different analytic models are proposed

Deep Learning Techniques for Mobility Prediction and Management in Mobile Networks

Trajectory prediction is an important research topic in modern mobile networks (e.g., 5G and beyond 5G) to enhance the network quality of service by accurately predicting the future locations of mobile users, such as pedestrians and vehicles, based on their past mobility patterns. A trajectory is defined as the sequence of locations the user visits over time. The primary objective of this thesis is to improve the modeling of mobility data and establish personalized, scalable, collective-intelligent, distributed, and strategic trajectory prediction techniques that can effectively adapt to the dynamics of urban environments in order to facilitate the optimal delivery of mobility-aware network services. Our proposed approaches aim to increase the accuracy of trajectory prediction while minimizing communication and computational costs leading to more efficient mobile networks. The thesis begins by introducing a personalized trajectory prediction technique using deep learning and reinforcement learning. It adapts the neural network architecture to capture the distinct characteristics of mobile users’ data. Furthermore, it introduces advanced anticipatory handover management and dynamic service migration techniques that optimize network management using our high-performance trajectory predictor. This approach ensures seamless connectivity and proactively migrates network services, enhancing the quality of service in dense wireless networks. The second contribution of the thesis introduces cluster-level prediction to extend the reinforcement learning-based trajectory prediction, addressing scalability challenges in large-scale networks. Cluster-level trajectory prediction leverages users’ similarities within clusters to train only a few representatives. This enables efficient transfer learning of pre-trained mobility models and reduces computational overhead enhancing the network scalability. The third contribution proposes a collaborative social-aware multi-agent trajectory prediction technique that accounts for the interactions between multiple intra-cluster agents in a dynamic urban environment, increasing the prediction accuracy but decreasing the algorithm complexity and computational resource usage. The fourth contribution proposes a federated learning-driven multi-agent trajectory prediction technique that leverages the collaborative power of multiple local data sources in a decentralized manner to enhance user privacy and improve the accuracy of trajectory prediction while jointly minimizing computational and communication costs. The fifth contribution proposes a game theoretic non-cooperative multi-agent prediction technique that considers the strategic behaviors among competitive inter-cluster mobile users. The proposed approaches are evaluated on small-scale and large-scale location-based mobility datasets, where locations could be GPS coordinates or cellular base station IDs. Our experiments demonstrate that our proposed approaches outperform state-of-the-art trajectory prediction methods making significant contributions to the field of mobile networks