53 research outputs found
Quality of experience and access network traffic management of HTTP adaptive video streaming
The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.Die Doktorarbeit beschäftigt sich mit Quality of Experience (QoE) – der subjektiv empfundenen Dienstgüte – von adaptivem HTTP Videostreaming (HAS) und mit Verkehrsmanagement, das in Zugangsnetzwerken eingesetzt werden kann, um die QoE des adaptiven Videostreamings zu verbessern. Zuerst wurde der Einfluss von Adaptionsparameters und der Zeit pro Qualitätsstufe auf die QoE von adaptivem Videostreaming mittels subjektiver Crowdsourcingstudien untersucht. Die Ergebnisse wurden benutzt, um die QoE-optimale Adaptionsstrategie für gegebene Videos und Netzwerkbedingungen zu berechnen. Dies ermöglicht Dienstanbietern von Videostreaming verbesserte Adaptionsstrategien für adaptives Videostreaming zu entwerfen und zu benchmarken. Weiterhin untersuchte die Arbeit Konzepte zum Überwachen von QoE von Videostreaming in der Applikation und im Netzwerk, die von Netzwerkbetreibern im Kreislauf des QoE-bewussten Verkehrsmanagements eingesetzt werden können. Außerdem wurde eine analytische und simulative Leistungsbewertung von QoE-bewusstem Verkehrsmanagement auf einer Engpassverbindung durchgeführt. Schließlich untersuchte diese Arbeit sozialbewusstes Verkehrsmanagement für adaptives Videostreaming mittels WLAN Offloading, also dem Auslagern von mobilen Videoflüssen über WLAN Netzwerke. Es wurde ein Modell für die Verteilung von öffentlichen WLAN Zugangspunkte und eine Plattform für sozialbewusstes Verkehrsmanagement auf privaten, häuslichen WLAN Routern vorgestellt. Abschließend untersuchte eine simulative Leistungsbewertung den Einfluss von WLAN Offloading auf die QoE und den Energieverbrauch von mobilem adaptivem Videostreaming
Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices
Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications
Fundamentals
Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters
Multi-Dimensional Resource Orchestration in Vehicular Edge Networks
In the era of autonomous vehicles, the advanced technologies of connected vehicle lead to the development of driving-related applications to meet the stringent safety requirements and the infotainment applications to improve passenger experience. Newly developed vehicular applications require high-volume data transmission, accurate sensing data collection, and reliable interaction, imposing substantial constrains on vehicular networks that solely rely on cellular networks to fetch data from the Internet and on-board processors to make driving decisions. To enhance multifarious vehicular applications, Heterogeneous Vehicular Networks (HVNets) have been proposed, in which edge nodes, including base stations and roadside units, can provide network connections, resulting in significantly reduced vehicular communication cost. In addition, caching servers are equipped at the edge nodes, to further alleviate the communication load for backhaul links and reduce data downloading delay. Hence, we aim to orchestrate the multi-dimensional resources, including communication, caching, and sensing resources, in the complex and dynamic vehicular environment to enhance vehicular edge network performance. The main technical issues are: 1) to accommodate the delivery services for both location-based and popular contents, the scheme of caching contents at edge servers should be devised, considering the cooperation of caching servers at different edge nodes, the mobility of vehicles, and the differential requirements of content downloading services; 2) to support the safety message exchange and collective perception services for vehicles, communication and sensing resources are jointly allocated, the decisions of which are coupled due to the resource sharing among different services and neighboring vehicles; and 3) for interaction-intensive service provisioning, e.g., trajectory design, the forwarding resources in core networks are allocated to achieve delay-sensitive packet transmissions between vehicles and management controllers, ensuring the high-quality interactivity. In this thesis, we design the multi-dimensional resource orchestration schemes in the edge assisted HVNets to address the three technical issues.
Firstly, we design a cooperative edge caching scheme to support various vehicular content downloading services, which allows vehicles to fetch one content from multiple caching servers cooperatively. In particular, we consider two types of vehicular content requests, i.e., location-based and popular contents, with different delay requirements. Both types of contents are encoded according to fountain code and cooperatively cached at multiple servers. The proposed scheme can be optimized by finding an optimal cooperative content placement that determines the placing locations and proportions for all contents. To this end, we analyze the upper bound proportion of content caching at a single server and provide the respective theoretical analysis of transmission delay and service cost (including content caching and transmission cost) for both types of contents. We then formulate an optimization problem of cooperative content placement to minimize the overall transmission delay and service cost. As the problem is a multi-objective multi-dimensional multi-choice knapsack one, which is proved to be NP-hard, we devise an ant colony optimization-based scheme to solve the problem and achieve a near-optimal solution. Simulation results are provided to validate the performance of the proposed scheme, including its convergence and optimality of caching, while guaranteeing low transmission delay and service cost.
Secondly, to support the vehicular safety message transmissions, we propose a two-level adaptive resource allocation (TARA) framework. In particular, three types of safety message are considered in urban vehicular networks, i.e., the event-triggered message for urgent condition warning, the periodic message for vehicular status notification, and the message for environmental perception. Roadside units are deployed for network management, and thus messages can be transmitted through either vehicle-to-infrastructure or vehicle-to-vehicle connections. To satisfy the requirements of different message transmissions, the proposed TARA framework consists of a group-level resource reservation module and a vehicle-level resource allocation module. Particularly, the resource reservation module is designed to allocate resources to support different types of message transmission for each vehicle group at the first level, and the group is formed by a set of neighboring vehicles. To learn the implicit relation between the resource demand and message transmission requests, a supervised learning model is devised in the resource reservation module, where to obtain the training data we further propose a sequential resource allocation (SRA) scheme. Based on historical network information, the SRA scheme offline optimizes the allocation of sensing resources (i.e., choosing vehicles to provide perception data) and communication resources. With the resource reservation result for each group, the vehicle-level resource allocation module is then devised to distribute specific resources for each vehicle to satisfy the differential requirements in real time. Extensive simulation results are provided to demonstrate the effectiveness of the proposed TARA framework in terms of the high successful reception ratio and low latency for message transmissions, and the high quality of collective environmental perception.
Thirdly, we investigate forwarding resource sharing scheme to support interaction intensive services in HVNets, especially for the delay-sensitive packet transmission between vehicles and management controllers. A learning-based proactive resource sharing scheme is proposed for core communication networks, where the available forwarding resources at a switch are proactively allocated to the traffic flows in order to maximize the efficiency of resource utilization with delay satisfaction. The resource sharing scheme consists of two joint modules: estimation of resource demands and allocation of available resources. For service provisioning, resource demand of each traffic flow is estimated based on the predicted packet arrival rate. Considering the distinct features of each traffic flow, a linear regression scheme is developed for resource demand estimation, utilizing the mapping relation between traffic flow status and required resources, upon which a network switch makes decision on allocating available resources for delay satisfaction and efficient resource utilization. To learn the implicit relation between the allocated resources and delay, a multi-armed bandit learning-based resource sharing scheme is proposed, which enables fast resource sharing adjustment to traffic arrival dynamics. The proposed scheme is proved to be asymptotically approaching the optimal strategy, with polynomial time complexity. Extensive simulation results are presented to demonstrate the effectiveness of the proposed resource sharing scheme in terms of delay satisfaction, traffic adaptiveness, and resource sharing gain.
In summary, we have investigated the cooperative caching placement for content downloading services, joint communication and sensing resource allocation for safety message transmissions, and forwarding resource sharing scheme in core networks for interaction intensive services. The schemes developed in the thesis should provide practical and efficient solutions to manage the multi-dimensional resources in vehicular networks
Towards Smart Vehicular Environments via Deep Learning and Emerging Technologies
Intelligent Transportation Systems (ITS) embrace smart vehicular environments through a fully connected paradigm known as vehicular networks. Vehicular networks allow automobiles to stay online and connected with their surroundings while travelling. In that sense, vehicular networks enable various activities; for example, autonomous driving, road surveillance, data collection, content delivery, and many others. This leads to more efficient, safer, and comfort driving experiences and opens up new opportunities for many business sectors. As such, the networking industry and academia have shown great interests in advancing vehicular networks and leveraging relevant services.
In this dissertation, several vehicular network problems are addressed along with proposing novel ideas and utilizing effective solutions. As opposed to stationary or slow moving communications, vehicular networks experience more challenging environment as a result of vehicle mobility. Consequently, vehicular networks suffer from ever-changing topology, short contact times, and intractable propagation environments. In particular, this dissertation presents six works that participate in supplementing the literature as follows. First, a content delivery framework in the context of vehicular network is studied where digital contents are generated by different content providers (CP) and have distinct values. To this end, a prefetching technique along with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communications are used to enable fast content delivery. Furthermore, a pricing model is proposed to deal with contents' values to attain a satisfactory Quality of Experience (QoE). Second, a more advanced system model is discussed to cache contents with the assistance of vehicles and to enable a disconnected and fixed Road-Side Unit (RSU) to participate in providing content delivery services. The changing popularity of contents is investigated besides accounting for the limited RSU cache capabilities. Third, the stationary RSU proposed in the second work is replaced by a more flexible infrastructure, namely an aerial RSU mounted on an unmanned aerial vehicle (UAV). The mobility of the UAV and its constrained energy capacity are analyzed and Deep Reinforcement Learning is incorporated to aid in solving the challenges in leveraging UAVs. Fourth, the previous two studies are integrated by investigating the collaboration between a UAV and terrestrial RSUs in delivering large-size contents. A strategy to fill up the UAV cache is also suggested via mulling contents over vehicles. Fifth, the complexity of vehicular urban environments is addressed. In particular, the problem of disconnected areas in vehicular environments due to the appearance of high-rise buildings and other obstacles is studied. In details, a Reconfigurable Intelligent Surface (RIS) is exploited to provide indirect links between the RSU and vehicles travelling through such areas. Our sixth and final contribution deals with time-constrained Internet of Things (IoT) devices (IoTD) supporting ITS networks. In this regard, a UAV is dispatched to collect their data timely and fully while being assisted by a RIS to improve the wireless channel quality. In the end, this dissertation provides discussions that highlight open research directions worth of further investigations
Mobile Ad-Hoc Networks
Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks
Multimedia
The nowadays ubiquitous and effortless digital data capture and processing capabilities offered by the majority of devices, lead to an unprecedented penetration of multimedia content in our everyday life. To make the most of this phenomenon, the rapidly increasing volume and usage of digitised content requires constant re-evaluation and adaptation of multimedia methodologies, in order to meet the relentless change of requirements from both the user and system perspectives. Advances in Multimedia provides readers with an overview of the ever-growing field of multimedia by bringing together various research studies and surveys from different subfields that point out such important aspects. Some of the main topics that this book deals with include: multimedia management in peer-to-peer structures & wireless networks, security characteristics in multimedia, semantic gap bridging for multimedia content and novel multimedia applications
Queueing-Theoretic End-to-End Latency Modeling of Future Wireless Networks
The fifth generation (5G) of mobile communication networks is envisioned to enable a variety of novel applications. These applications demand requirements from the network, which are diverse and challenging. Consequently, the mobile network has to be not only capable to meet the demands of one of these applications, but also be flexible enough that it can be tailored to different needs of various services. Among these new applications, there are use cases that require low latency as well as an ultra-high reliability, e.g., to ensure unobstructed production in factory automation or road safety for (autonomous) transportation. In these domains, the requirements are crucial, since violating them may lead to financial or even human damage. Hence, an ultra-low probability of failure is necessary.
Based on this, two major questions arise that are the motivation for this thesis. First, how can ultra-low failure probabilities be evaluated, since experiments or simulations would require a tremendous number of runs and, thus, turn out to be infeasible. Second, given a network that can be configured differently for different applications through the concept of network slicing, which performance can be expected by different parameters and what is their optimal choice, particularly in the presence of other applications.
In this thesis, both questions shall be answered by appropriate mathematical modeling of the radio interface and the radio access network. Thereby the aim is to find the distribution of the (end-to-end) latency, allowing to extract stochastic measures such as the mean, the variance, but also ultra-high percentiles at the distribution tail. The percentile analysis eventually leads to the desired evaluation of worst-case scenarios at ultra-low probabilities. Therefore, the mathematical tool of queuing theory is utilized to study video streaming performance and one or multiple (low-latency) applications. One of the key contributions is the development of a numeric algorithm to obtain the latency of general queuing systems for homogeneous as well as for prioritized heterogeneous traffic. This provides the foundation for analyzing and improving end-to-end latency for
applications with known traffic distributions in arbitrary network topologies and consisting of one or multiple network slices.Es wird erwartet, dass die fünfte Mobilfunkgeneration (5G) eine Reihe neuartiger Anwendungen ermöglichen wird. Allerdings stellen diese Anwendungen sowohl sehr unterschiedliche als auch überaus herausfordernde Anforderungen an das Netzwerk. Folglich muss das mobile Netz nicht nur die Voraussetzungen einer einzelnen Anwendungen erfüllen, sondern auch flexibel genug sein, um an die Vorgaben unterschiedlicher Dienste angepasst werden zu können. Ein Teil der neuen Anwendungen erfordert hochzuverlässige Kommunikation mit niedriger Latenz, um beispielsweise unterbrechungsfreie Produktion in der Fabrikautomatisierung oder Sicherheit im (autonomen) Straßenverkehr zu gewährleisten. In diesen Bereichen ist die Erfüllung der gestellten Anforderungen besonders kritisch, da eine Verletzung finanzielle oder sogar personelle Schäden nach sich ziehen könnte. Eine extrem niedrige Ausfallwahrscheinlichkeit ist daher von größter Wichtigkeit.
Daraus ergeben sich zwei wesentliche Fragestellungen, welche diese Arbeit motivieren. Erstens, wie können extrem niedrige Ausfallwahrscheinlichkeiten evaluiert werden. Ihr Nachweis durch Experimente oder Simulationen würde eine extrem große Anzahl an Durchläufen benötigen und sich daher als nicht realisierbar herausstellen. Zweitens, welche Performanz ist für ein gegebenes Netzwerk durch unterschiedliche Konfigurationen zu erwarten und wie kann die optimale Konfiguration gewählt werden. Diese Frage ist insbesondere dann interessant, wenn mehrere Anwendungen gleichzeitig bedient werden und durch sogenanntes Slicing für jeden Dienst unterschiedliche Konfigurationen möglich sind.
In dieser Arbeit werden beide Fragen durch geeignete mathematische Modellierung der Funkschnittstelle sowie des Funkzugangsnetzes (Radio Access Network) adressiert. Mithilfe der Warteschlangentheorie soll die stochastische Verteilung der (Ende-zu-Ende-) Latenz bestimmt werden. Dies liefert unterschiedliche stochastische Metriken, wie den Erwartungswert, die Varianz und insbesondere extrem hohe Perzentile am oberen Rand der Verteilung. Letztere geben schließlich Aufschluss über die gesuchten schlimmsten Fälle, die mit sehr geringer Wahrscheinlichkeit eintreten können. In der Arbeit werden Videostreaming und ein oder mehrere niedriglatente Anwendungen untersucht. Zu den wichtigsten Beiträgen zählt dabei die Entwicklung einer numerischen Methode, um die Latenz in allgemeinen Warteschlangensystemen für homogenen sowie für priorisierten heterogenen Datenverkehr zu bestimmen. Dies legt die Grundlage für die Analyse und Verbesserung von Ende-zu-Ende-Latenz für Anwendungen mit bekannten Verkehrsverteilungen in beliebigen Netzwerktopologien mit ein oder mehreren Slices
Social Cooperation for Information-Centric Multimedia Streaming in Highway VANETs
Abstract-High-quality multimedia streaming services in Vehicular Ad-hoc Networks (VANETs) are severely hindered by intermittent host connectivity issues. The Information Centric Networking (ICN) paradigm could help solving this issue thanks to its new networking primitives driven by content names rather than host addresses. This unique feature, in fact, enables native support to mobility, in-network caching, nomadic networking, multicast, and efficient content dissemination. In this paper, we focus on exploring the potential social cooperation among vehicles in highways. An ICN-based COoperative Caching solution, namely ICoC, is proposed to improve the quality of experience (QoE) of multimedia streaming services. In particular, ICoC leverages two novel social cooperation schemes, namely partner-assisted and courier-assisted, to enhance information-centric caching. To validate its effectiveness, extensive ns-3 simulations have been executed, showing that ICoC achieves a considerable improvement in terms of start-up delay and playback freezing with respect to a state-of-the-art solution based on probabilistic caching
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