19 research outputs found
Video Quality Prediction for Video over Wireless Access Networks (UMTS and WLAN)
Transmission of video content over wireless access networks (in particular, Wireless Local
Area Networks (WLAN) and Third Generation Universal Mobile Telecommunication System (3G UMTS)) is growing exponentially and gaining popularity, and is predicted to expose new revenue streams for mobile network operators. However, the success of these video applications over wireless access networks very much depend on meeting the user’s Quality of Service (QoS) requirements. Thus, it is highly desirable to be able to predict and, if appropriate, to control video quality to meet user’s QoS requirements. Video quality is
affected by distortions caused by the encoder and the wireless access network. The impact of these distortions is content dependent, but this feature has not been widely used in existing
video quality prediction models.
The main aim of the project is the development of novel and efficient models for video
quality prediction in a non-intrusive way for low bitrate and resolution videos and to
demonstrate their application in QoS-driven adaptation schemes for mobile video streaming
applications. This led to five main contributions of the thesis as follows:(1) A thorough understanding of the relationships between video quality, wireless access network (UMTS and WLAN) parameters (e.g. packet/block loss, mean burst length
and link bandwidth), encoder parameters (e.g. sender bitrate, frame rate) and content type is provided. An understanding of the relationships and interactions between them
and their impact on video quality is important as it provides a basis for the development of non-intrusive video quality prediction models.(2) A new content classification method was proposed based on statistical tools as content
type was found to be the most important parameter.
(3) Efficient regression-based and artificial neural network-based learning models were
developed for video quality prediction over WLAN and UMTS access networks. The
models are light weight (can be implemented in real time monitoring), provide a measure for user perceived quality, without time consuming subjective tests. The models have potential applications in several other areas, including QoS control and
optimization in network planning and content provisioning for network/service
providers.(4) The applications of the proposed regression-based models were investigated in (i)
optimization of content provisioning and network resource utilization and (ii) A new
fuzzy sender bitrate adaptation scheme was presented at the sender side over WLAN and UMTS access networks.
(5) Finally, Internet-based subjective tests that captured distortions caused by the encoder
and the wireless access network for different types of contents were designed. The
database of subjective results has been made available to research community as there is a lack of subjective video quality assessment databases.Partially sponsored by EU FP7 ADAMANTIUM Project (EU Contract 214751
An adaptive framework for end-to-end quality of service management
Ph.DDOCTOR OF PHILOSOPH
Seamless multimedia delivery within a heterogeneous wireless networks environment: are we there yet?
The increasing popularity of live video streaming from mobile devices such as Facebook Live, Instagram Stories, Snapchat, etc. pressurises the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of Quality of Experience (QoE) as the basis for network control, customer loyalty and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users’ quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: adaptation, energy efficiency and multipath content delivery. Discussions, challenges and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided
Seamless Multimedia Delivery Within a Heterogeneous Wireless Networks Environment: Are We There Yet?
The increasing popularity of live video streaming from mobile devices, such as Facebook Live, Instagram Stories, Snapchat, etc. pressurizes the network operators to increase the capacity of their networks. However, a simple increase in system capacity will not be enough without considering the provisioning of quality of experience (QoE) as the basis for network control, customer loyalty, and retention rate and thus increase in network operators revenue. As QoE is gaining strong momentum especially with increasing users' quality expectations, the focus is now on proposing innovative solutions to enable QoE when delivering video content over heterogeneous wireless networks. In this context, this paper presents an overview of multimedia delivery solutions, identifies the problems and provides a comprehensive classification of related state-of-the-art approaches following three key directions: 1) adaptation; 2) energy efficiency; and 3) multipath content delivery. Discussions, challenges, and open issues on the seamless multimedia provisioning faced by the current and next generation of wireless networks are also provided
Investigating the Effects of Network Dynamics on Quality of Delivery Prediction and Monitoring for Video Delivery Networks
Video streaming over the Internet requires an optimized delivery system given the advances in network architecture, for example, Software Defined Networks. Machine Learning (ML) models have been deployed in an attempt to predict the quality of the video streams. Some of these efforts have considered the prediction of Quality of Delivery (QoD) metrics of the video stream in an effort to measure the quality of the video stream from the network perspective. In most cases, these models have either treated the ML algorithms as black-boxes or failed to capture the network dynamics of the associated video streams.
This PhD investigates the effects of network dynamics in QoD prediction using ML techniques. The hypothesis that this thesis investigates is that ML techniques that model the underlying network dynamics achieve accurate QoD and video quality predictions and measurements. The thesis results demonstrate that the proposed techniques offer performance gains over approaches that fail to consider network dynamics. This thesis results highlight that adopting the correct model by modelling the dynamics of the network infrastructure is crucial to the accuracy of the ML predictions. These results are significant as they demonstrate that improved performance is achieved at no additional computational or storage cost. These techniques can help the network manager, data center operatives and video service providers take proactive and corrective actions for improved network efficiency and effectiveness
An energy efficient http adaptive streaming protocol design for mobile hand-held devices
Internet traffic generated from mobile devices has experienced a huge growth in the last few years. With the increasing popularity of streaming applications in mobile devices, video traffic generated from mobile devices is also increasing. One of the big challenges of streaming applications on mobile devices is the energy intensive behaviour of such applications. Energy management has always been a critical issue for mobile devices. A wireless network interface consumes a significant portion of the total system energy while active. During video streaming, the network interface is kept awake for a long period of time. This causes a large energy drain. There are several research works focused on reducing energy consumption during video streaming on mobile devices.
HTTP adaptive streaming is gaining popularity as a method of video delivery because of its significant advantages in terms of both user-perceived quality and resource utilization. By using rate adaptation via changes in the requested video version, it adapts to varying network available capacity. There are several research work that aim to increase the performance of rate adaptation. None of the previous works have focused on reducing energy consumption during HTTP adaptive streaming.
In this thesis, an energy efficient HTTP adaptive streaming protocol is designed. The new protocol uses an efficient buffer management approach and a three step bitrate selection mechanism. The proposed protocol is implemented by modifying the Adobe OSMF player version 1.6. Performance evaluation of the new protocol is carried out by running a number of experiments in both a lab environment and three real world environments. The experimental results show that the proposed protocol is able to achieve high amounts of sleep time (by more than an estimated 70% for WiFi and more than 35% for 3G/EDGE) and reduce energy consumption during data transfer. It can also reduce data wastage by 80% in case of playback interruption in the video playback
User-centric power-friendly quality-based network selection strategy for heterogeneous wireless environments
The ‘Always Best Connected’ vision is built around the scenario of a mobile user seamlessly roaming within a multi-operator multi-technology multi-terminal multi-application
multi-user environment supported by the next generation of wireless networks. In this heterogeneous environment, users equipped with multi-mode wireless mobile devices will
access rich media services via one or more access networks. All these access networks may differ in terms of technology, coverage range, available bandwidth, operator, monetary cost, energy usage etc. In this context, there is a need for a smart network selection decision to be made, to choose the best available network option to cater for the user’s current application and requirements. The decision is a difficult one, especially given the number and dynamics of the possible input parameters. What parameters are used and how those parameters model the application requirements and user needs is important. Also, game theory approaches can be used to model and analyze the cooperative or competitive interaction between the rational decision makers involved, which are users, seeking to get good service quality at good value prices, and/or the network operators, trying to increase their revenue.
This thesis presents the roadmap towards an ‘Always Best Connected’ environment. The proposed solution includes an Adapt-or-Handover solution which makes use of a Signal
Strength-based Adaptive Multimedia Delivery mechanism (SAMMy) and a Power-Friendly Access Network Selection Strategy (PoFANS) in order to help the user in taking
decisions, and to improve the energy efficiency at the end-user mobile device. A Reputation-based System is proposed, which models the user-network interaction as a repeated cooperative game following the repeated Prisoner’s Dilemma game from Game Theory. It combines reputation-based systems, game theory and a network selection mechanism in order to create a reputation-based heterogeneous environment. In this environment, the users keep track of their individual history with the visited networks. Every time, a user connects to a network the user-network interaction game is played. The outcome of the game is a network reputation factor which reflects the network’s previous behavior in assuring service guarantees to the user. The network reputation factor will impact the decision taken by the user next time, when he/she will have to decide whether to connect or not to that specific network. The performance of the proposed solutions was evaluated through in-depth analysis and both simulation-based and experimental-oriented testing. The results clearly show improved performance of the proposed solutions in comparison with other similar state-of-the-art solutions. An energy consumption study for a Google Nexus One streaming adaptive multimedia was performed, and a comprehensive survey on related Game Theory research are provided as part of the work
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