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

Metrics for Broadband Networks in the Context of the Digital Economies

In a transition to automated digital management of broadband networks, communication service providers must look for new metrics to monitor these networks. Complete metrics frameworks are already emerging whereas majority of the new metrics are being proposed in technical papers. Considering common metrics for broadband networks and related technologies, this chapter offers insights into what metrics are available, and also suggests active areas of research. The broadband networks being a key component of the digital ecosystems are also an enabler to many other digital technologies and services. Reviewing first the metrics for computing systems, websites and digital platforms, the chapter focus then shifts to the most important technical and business metrics which are used for broadband networks. The demand-side and supply-side metrics including the key metrics of broadband speed and broadband availability are touched on. After outlining the broadband metrics which have been standardized and the metrics for measuring Internet traffic, the most commonly used metrics for broadband networks are surveyed in five categories: energy and power metrics, quality of service, quality of experience, security metrics, and robustness and resilience metrics. The chapter concludes with a discussion on machine learning, big data and the associated metrics

Experimentation and Characterization of Mobile Broadband Networks

The Internet has brought substantial changes to our life as the main tool to access a large variety of services and applications. Internet distributed nature and technological improvements lead to new challenges for researchers, service providers, and network administrators. Internet traffic measurement and analysis is one of the most trivial and powerful tools to study such a complex environment from different aspects. Mobile BroadBand (MBB) networks have become one of the main means to access the Internet. MBB networks are evolving at a rapid pace with technology enhancements that promise drastic improvements in capacity, connectivity, and coverage, i.e., better performance in general. Open experimentation with operational MBB networks in the wild is currently a fundamental requirement of the research community in its endeavor to address the need for innovative solutions for mobile communications. There is a strong need for objective data relating to stability and performance of MBB (e.g., 2G, 3G, 4G, and soon-to-come 5G) networks and for tools that rigorously and scientifically assess their performance. Thus, measuring end user performance in such an environment is a challenge that calls for large-scale measurements and profound analysis of the collected data. The intertwining of technologies, protocols, and setups makes it even more complicated to design scientifically sound and robust measurement campaigns. In such a complex scenario, the randomness of the wireless access channel coupled with the often unknown operator configurations makes this scenario even more challenging. In this thesis, we introduce the MONROE measurement platform: an open access and flexible hardware-based platform for measurements on operational MBB networks. The MONROE platform enables accurate, realistic, and meaningful assessment of the performance and reliability of MBB networks. We detail the challenges we overcame while building and testing the MONROE testbed and argue our design and implementation choices accordingly. Measurements are designed to stress performance of MBB networks at different network layers by proposing scalable experiments and methodologies. We study: (i) Network layer performance, characterizing and possibly estimating the download speed offered by commercial MBB networks; (ii) End users’ Quality of Experience (QoE), specifically targeting the web performance of HTTP1.1/TLS and HTTP2 on various popular web sites; (iii) Implication of roaming in Europe, understanding the roaming ecosystem in Europe after the "Roam like Home" initiative; and (iv) A novel adaptive scheduler family with deadline is proposed for multihomed devices that only require a very coarse knowledge of the wireless bandwidth. Our results comprise different contributions in the scope of each research topic. To put it in a nutshell, we pinpoint the impact of different network configurations that further complicate the picture and hopefully contribute to the debate about performance assessment in MBB networks. The MBB users web performance shows that HTTP1.1/TLS is very similar to HTTP2 in our large-scale measurements. Furthermore, we observe that roaming is well supported for the monitored operators and the operators using the same approach for routing roaming traffic. The proposed adaptive schedulers for content upload in multihomed devices are evaluated in both numerical simulations and real mobile nodes. Simulation results show that the adaptive solutions can effectively leverage the fundamental tradeoff between the upload cost and completion time, despite unpredictable variations in available bandwidth of wireless interfaces. Experiments in the real mobile nodes provided by the MONROE platform confirm the findings

Investigating optimal internet data collection in low resource networks

Community networks have been proposed by many networking experts and researchers as a way to bridge the connectivity gaps in rural and remote areas of the world. Many community networks are built with low-capacity computing devices and low-capacity links. Such community networks are examples of low resource networks. The design and implementation of computer networks using limited hardware and software resources has been studied extensively in the past, but scheduling strategies for conducting measurements on these networks remains an important area to be explored. In this study, the design of a Quality of Service monitoring system is proposed, focusing on performance of scheduling of network measurement jobs in different topologies of a low-resource network. We also propose a virtual network testbed and perform evaluations of the system under varying measurement specifications. Our results show that the system is capable of completing almost 100% of the measurements that are launched by users. Additionally, we found that the error due to contention for network resources among measurements stays constant at approximately 34% with increasing number of measurement nodes