Maximizing Resource Utilization In Video Streaming Systems

Video streaming has recently grown dramatically in popularity over the Internet, Cable TV, and wire-less networks. Because of the resource demanding nature of video streaming applications, maximizing resource utilization in any video streaming system is a key factor to increase the scalability and decrease the cost of the system. Resources to utilize include server bandwidth, network bandwidth, battery life in battery operated devices, and processing time in limited processing power devices. In this work, we propose new techniques to maximize the utilization of video-on-demand (VOD) server resources. In addition to that, we propose new framework to maximize the utilization of the network bandwidth in wireless video streaming systems. Providing video streaming users in a VOD system with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait thereby increasing server utilization by increasing server throughput. In this work, we analyze waiting-time predictability in scalable video streaming. We also propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits. The achieved resource sharing by stream merging depends greatly on how the waiting requests are scheduled for service. Motivated by the development of cost-based scheduling, we investigate its effectiveness in great detail and discuss opportunities for further tunings and enhancements. Additionally, we analyze the effectiveness of incorporating video prediction results into the scheduling decisions. We also study the interaction between scheduling policies and the stream merging techniques and explore new ways for enhancements. The interest in video surveillance systems has grown dramatically during the last decade. Auto-mated video surveillance (AVS) serves as an efficient approach for the realtime detection of threats and for monitoring their progress. Wireless networks in AVS systems have limited available bandwidth that have to be estimated accurately and distributed efficiently. In this research, we develop two cross-layer optimization frameworks that maximize the bandwidth optimization of 802.11 wireless network. We develop a distortion-based cross-layer optimization framework that manages bandwidth in the wire-less network in such a way that minimizes the overall distortion. We also develop an accuracy-based cross-layer optimization framework in which the overall detection accuracy of the computer vision algorithm(s) running in the system is maximized. Both proposed frameworks manage the application rates and transmission opportunities of various video sources based on the dynamic network conditions to achieve their goals. Each framework utilizes a novel online approach for estimating the effective airtime of the network. Moreover, we propose a bandwidth pruning mechanism that can be used with the accuracy-based framework to achieve any desired tradeoff between detection accuracy and power consumption. We demonstrate the effectiveness of the proposed frameworks, including the effective air-time estimation algorithms and the bandwidth pruning mechanism, through extensive experiments using OPNET

On service optimization in community network micro-clouds

Cotutela Universitat Politècnica de Catalunya i KTH Royal Institute of TechnologyInternet coverage in the world is still weak and local communities are required to come together and build their own network infrastructures. People collaborate for the common goal of accessing the Internet and cloud services by building Community networks (CNs). The use of Internet cloud services has grown over the last decade. Community network cloud infrastructures (i.e. micro-clouds) have been introduced to run services inside the network, without the need to consume them from the Internet. CN micro-clouds aims for not only an improved service performance, but also an entry point for an alternative to Internet cloud services in CNs. However, the adaptation of the services to be used in CN micro-clouds have their own challenges since the use of low-capacity devices and wireless connections without a central management is predominant in CNs. Further, large and irregular topology of the network, high software and hardware diversity and different service requirements in CNs, makes the CN micro-clouds a challenging environment to run local services, and to achieve service performance and quality similar to Internet cloud services. In this thesis, our main objective is the optimization of services (performance, quality) in CN micro-clouds, facilitating entrance to other services and motivating members to make use of CN micro-cloud services as an alternative to Internet services. We present an approach to handle services in CN micro-cloud environments in order to improve service performance and quality that can be approximated to Internet services, while also giving to the community motivation to use CN micro-cloud services. Furthermore, we break the problem into different levels (resource, service and middleware), propose a model that provides improvements for each level and contribute with information that helps to support the improvements (in terms of service performance and quality) in the other levels. At the resource level, we facilitate the use of community devices by utilizing virtualization techniques that isolate and manage CN micro-cloud services in order to have a multi-purpose environment that fosters services in the CN micro-cloud environment. At the service level, we build a monitoring tool tailored for CN micro-clouds that helps us to analyze service behavior and performance in CN micro-clouds. Subsequently, the information gathered enables adaptation of the services to the environment in order to improve their quality and performance under CN environments. At the middleware level, we build overlay networks as the main communication system according to the social information in order to improve paths and routes of the nodes, and improve transmission of data across the network by utilizing the relationships already established in the social network or community of practices that are related to the CNs. Therefore, service performance in CN micro-clouds can become more stable with respect to resource usage, performance and user perceived quality.Acceder a Internet sigue siendo un reto en muchas partes del mundo y las comunidades locales se ven en la necesidad de colaborar para construir sus propias infraestructuras de red. Los usuarios colaboran por el objetivo común de acceder a Internet y a los servicios en la nube construyendo redes comunitarias (RC). El uso de servicios de Internet en la nube ha crecido durante la última década. Las infraestructuras de nube en redes comunitarias (i.e., micronubes) han aparecido para albergar servicios dentro de las mismas redes, sin tener que acceder a Internet para usarlos. Las micronubes de las RC no solo tienen por objetivo ofrecer un mejor rendimiento, sino también ser la puerta de entrada en las RC hacia una alternativa a los servicios de Internet en la nube. Sin embargo, la adaptación de los servicios para ser usados en micronubes de RC conlleva sus retos ya que el uso de dispositivos de recursos limitados y de conexiones inalámbricas sin una gestión centralizada predominan en las RC. Más aún, la amplia e irregular topología de la red, la diversidad en el hardware y el software y los diferentes requisitos de los servicios en RC convierten en un desafío albergar servicios locales en micronubes de RC y obtener un rendimiento y una calidad del servicio comparables a los servicios de Internet en la nube. Esta tesis tiene por objetivo la optimización de servicios (rendimiento, calidad) en micronubes de RC, facilitando la entrada a otros servicios y motivando a sus miembros a usar los servicios en la micronube de RC como una alternativa a los servicios en Internet. Presentamos una aproximación para gestionar los servicios en entornos de micronube de RC para mejorar su rendimiento y calidad comparable a los servicios en Internet, a la vez que proporcionamos a la comunidad motivación para usar los servicios de micronube en RC. Además, dividimos el problema en distintos niveles (recursos, servicios y middleware), proponemos un modelo que proporciona mejoras para cada nivel y contribuye con información que apoya las mejoras (en términos de rendimiento y calidad de los servicios) en los otros niveles. En el nivel de los recursos, facilitamos el uso de dispositivos comunitarios al emplear técnicas de virtualización que aíslan y gestionan los servicios en micronubes de RC para obtener un entorno multipropósito que fomenta los servicios en el entorno de micronube de RC. En el nivel de servicio, construimos una herramienta de monitorización a la medida de las micronubes de RC que nos ayuda a analizar el comportamiento de los servicios y su rendimiento en micronubes de RC. Luego, la información recopilada permite adaptar los servicios al entorno para mejorar su calidad y rendimiento bajo las condiciones de una RC. En el nivel de middleware, construimos redes de overlay que actúan como el sistema de comunicación principal de acuerdo a información social para mejorar los caminos y las rutas de los nodos y mejoramos la transmisión de datos a lo largo de la red al utilizar las relaciones preestablecidas en la red social o la comunidad de prácticas que están relacionadas con las RC. De este modo, el rendimiento en las micronubes de RC puede devenir más estable respecto al uso de recursos, el rendimiento y la calidad percibidas por el usuario.Postprint (published version

A Survey of Anticipatory Mobile Networking: Context-Based Classification, Prediction Methodologies, and Optimization Techniques

A growing trend for information technology is to not just react to changes, but anticipate them as much as possible. This paradigm made modern solutions, such as recommendation systems, a ubiquitous presence in today's digital transactions. Anticipatory networking extends the idea to communication technologies by studying patterns and periodicity in human behavior and network dynamics to optimize network performance. This survey collects and analyzes recent papers leveraging context information to forecast the evolution of network conditions and, in turn, to improve network performance. In particular, we identify the main prediction and optimization tools adopted in this body of work and link them with objectives and constraints of the typical applications and scenarios. Finally, we consider open challenges and research directions to make anticipatory networking part of next generation networks

Towards More Efficient 5G Networks via Dynamic Traffic Scheduling

Department of Electrical EngineeringThe 5G communications adopt various advanced technologies such as mobile edge computing and unlicensed band operations, to meet the goal of 5G services such as enhanced Mobile Broadband (eMBB) and Ultra Reliable Low Latency Communications (URLLC). Specifically, by placing the cloud resources at the edge of the radio access network, so-called mobile edge cloud, mobile devices can be served with lower latency compared to traditional remote-cloud based services. In addition, by utilizing unlicensed spectrum, 5G can mitigate the scarce spectrum resources problem thus leading to realize higher throughput services. To enhance user-experienced service quality, however, aforementioned approaches should be more fine-tuned by considering various network performance metrics altogether. For instance, the mechanisms for mobile edge computing, e.g., computation offloading to the edge cloud, should not be optimized in a specific metric's perspective like latency, since actual user satisfaction comes from multi-domain factors including latency, throughput, monetary cost, etc. Moreover, blindly combining unlicensed spectrum resources with licensed ones does not always guarantee the performance enhancement, since it is crucial for unlicensed band operations to achieve peaceful but efficient coexistence with other competing technologies (e.g., Wi-Fi). This dissertation proposes a focused resource management framework for more efficient 5G network operations as follows. First, Quality-of-Experience is adopted to quantify user satisfaction in mobile edge computing, and the optimal transmission scheduling algorithm is derived to maximize user QoE in computation offloading scenarios. Next, regarding unlicensed band operations, two efficient mechanisms are introduced to improve the coexistence performance between LTE-LAA and Wi-Fi networks. In particular, we develop a dynamic energy-detection thresholding algorithm for LTE-LAA so that LTE-LAA devices can detect Wi-Fi frames in a lightweight way. In addition, we propose AI-based network configuration for an LTE-LAA network with which an LTE-LAA operator can fine-tune its coexistence parameters (e.g., CAA threshold) to better protect coexisting Wi-Fi while achieving enhanced performance than the legacy LTE-LAA in the standards. Via extensive evaluations using computer simulations and a USRP-based testbed, we have verified that the proposed framework can enhance the efficiency of 5G.clos

Hybrid Radio Resource Management for Heterogeneous Wireless Access Network

Heterogeneous wireless access network (HWAN) is composed of fifth-generation (5G) and fourth-generation (4G) cellular systems and IEEE 802.11-based wireless local area networks (WLANs). These diverse and dense wireless networks have different data rates, coverage, capacity, cost, and QoS. Furthermore, user devices are multi-modal devices that allow users to connect to more than one network simultaneously. This thesis presents radio resource management for RAT selection, radio resource allocation, load balancing, congestion control mechanism, and user device (UD) energy management that can effectively utilize the available resources in the heterogeneous wireless networks and enhance the quality-of-service (QoS) and user quality-of-experience (QoE). Recent studies on radio resource management in HWAN lead to two broad categories, 1) centralized architecture and 2) distributed model. In the centralized model, all the decision making power confines to a centralized controller and user devices are assumed as passive transceivers. In contrast, user devices actively participate in radio resource management in the distributed model, resulting in poor resource utilization and maximum call blocking and call dropping probabilities. In this thesis, we present a novel hybrid radio resource management model for HWAN that is composed of OFDMA based system and WLAN. In this model, both the centralized controller and the user device take part in resource management. Our hybrid mechanism considers attributes related to both user and network. However, these attributes are conflicting in nature. Moreover, a single RAT selection is performed based on user location and available networks, whereas UD with a multi-homing call receives the radio resource share from each network to fulfil its minimum data rate requirement. A novel approach is proposed for load balancing where an equal load ratio is maintained across all the available networks in HWAN. Performance evaluation through call blocking probability and network utilization will reveal the effectiveness of the proposed scheme. The demand for more data rates is on the rise. The 5G heterogeneous wireless access network is a potential solution to tackle the high data rate demand. The 5GHWAN is composed of 5G new radio (NR) and 4G long-term evolution (LTE) base stations (BSs). In a practical system, the channel conditions fluctuate due to user mobility. We, therefore, investigate radio resource allocation and congestion control mechanism along with network-assisted distributive RAT selection in a time-varying 5GHWAN. This joint problem of radio resource allocation and congestion control management has signalling overhead and computational complexity limitations. Therefore, we use the Lyapunov optimization to convert the offline problem into an online optimization problem based on channel state information (CSI) and queue state information (QSI). The theoretical and simulation results evaluate the performance of our proposed approach under the assumption of network stability. In addition, simulation results are presented to depict our proposed scheme’s effectiveness. Furthermore, our proposed RAT selection scheme performs better than the traditional centralized and distributive mechanisms. Recently an increase in the usage of video applications has been observed. Therefore, we explore hybrid radio resource management video streaming over time-varying HWAN. Using the Lyapunov optimization technique, we decompose our two-time scale stochastic optimization problem into two main sub-problems. One of the sub-problems is related to radio resource allocation that operates at a scheduling time interval. The radio resource allocation policy is implemented at a centralized control node responsible for allocating radio resources from the available wireless networks using Lagrange dual method. The other sub-problem is related to the quality rate adaptation policy that works at a chunk time scale. Each user selects the appropriate quality level of the video chunks adaptively in a distributive way based on buffer state and channel state information. We analyze and compare the QoE of our proposed approach over an arbitrary sample path of channel state information with an optimal T-slot algorithm. Finally, we evaluate the performance analysis of our proposed scheme for video streaming over a time-varying heterogeneous wireless access network through simulation results

Prediction-based techniques for the optimization of mobile networks

Mención Internacional en el título de doctorMobile cellular networks are complex system whose behavior is characterized by the superposition of several random phenomena, most of which, related to human activities, such as mobility, communications and network usage. However, when observed in their totality, the many individual components merge into more deterministic patterns and trends start to be identifiable and predictable. In this thesis we analyze a recent branch of network optimization that is commonly referred to as anticipatory networking and that entails the combination of prediction solutions and network optimization schemes. The main intuition behind anticipatory networking is that knowing in advance what is going on in the network can help understanding potentially severe problems and mitigate their impact by applying solution when they are still in their initial states. Conversely, network forecast might also indicate a future improvement in the overall network condition (i.e. load reduction or better signal quality reported from users). In such a case, resources can be assigned more sparingly requiring users to rely on buffered information while waiting for the better condition when it will be more convenient to grant more resources. In the beginning of this thesis we will survey the current anticipatory networking panorama and the many prediction and optimization solutions proposed so far. In the main body of the work, we will propose our novel solutions to the problem, the tools and methodologies we designed to evaluate them and to perform a real world evaluation of our schemes. By the end of this work it will be clear that not only is anticipatory networking a very promising theoretical framework, but also that it is feasible and it can deliver substantial benefit to current and next generation mobile networks. In fact, with both our theoretical and practical results we show evidences that more than one third of the resources can be saved and even larger gain can be achieved for data rate enhancements.Programa Oficial de Doctorado en Ingeniería TelemáticaPresidente: Albert Banchs Roca.- Presidente: Pablo Serrano Yañez-Mingot.- Secretario: Jorge Ortín Gracia.- Vocal: Guevara Noubi

Resource management in QoS-aware wireless cellular networks

2011 Summer.Includes bibliographical references.Emerging broadband wireless networks that support high speed packet data with heterogeneous quality of service (QoS) requirements demand more flexible and efficient use of the scarce spectral resource. Opportunistic scheduling exploits the time-varying, location-dependent channel conditions to achieve multiuser diversity. In this work, we study two types of resource allocation problems in QoS-aware wireless cellular networks. First, we develop a rigorous framework to study opportunistic scheduling in multiuser OFDM systems. We derive optimal opportunistic scheduling policies under three common QoS/fairness constraints for multiuser OFDM systems--temporal fairness, utilitarian fairness, and minimum-performance guarantees. To implement these optimal policies efficiently, we provide a modified Hungarian algorithm and a simple suboptimal algorithm. We then propose a generalized opportunistic scheduling framework that incorporates multiple mixed QoS/fairness constraints, including providing both lower and upper bound constraints. Next, taking input queues and channel memory into consideration, we reformulate the transmission scheduling problem as a new class of Markov decision processes (MDPs) with fairness constraints. We investigate the throughput maximization and the delay minimization problems in this context. We study two categories of fairness constraints, namely temporal fairness and utilitarian fairness. We consider two criteria: infinite horizon expected total discounted reward and expected average reward. We derive and prove explicit dynamic programming equations for the above constrained MDPs, and characterize optimal scheduling policies based on those equations. An attractive feature of our proposed schemes is that they can easily be extended to fit different objective functions and other fairness measures. Although we only focus on uplink scheduling, the scheme is equally applicable to the downlink case. Furthermore, we develop an efficient approximation method--temporal fair rollout--to reduce the computational cost