The main goal of today’s wireless Service Providers (SPs) is to provide optimum and ubiquitous service for roaming users while maximizing the SPs own monetary profits. The fundamental objective is to support such requirements by providing solutions that are adaptive to varying conditions in highly mobile and heterogeneous, as well as dynamically changing wireless network infrastructures. This can only be achieved through well-designed management systems. Most techniques fail to utilize the knowledge gained from previously tested reconfiguration strategies on system and network behaviour. This dissertation presents a novel framework that automates the cooperation among a number of wireless SPs facing the challenge of meeting strict service demands for a large number of mobile users. The proposed work employs a novel policy-based system configuration model to automate the process of adapting new network policies. The proposed framework relies on the assistance of a real-time simulator that runs as a constant background process in order to continuously find optimal policy configurations for the SPs’ networks. To minimize the computational time needed to find these configurations, a modified tabu-search scheme is proposed. An objective is to efficiently explore the space of network configurations in order to find optimal network decisions and provide a service performance that adheres to contracted service level agreements. This framework also relies on a distributed Quality of Service (QoS) monitoring scheme. The proposed scheme relies on the efficient identification of candidate QoS monitoring users that can efficiently submit QoS related measurements on behalf of their neighbors. These candidate users are chosen according to their devices’ residual power and transmission capabilities and their estimated remaining service lifetime. Service monitoring users are then selected from these candidates using a novel user-to-user semantic similarity matching algorithm. This step ensures that the monitoring users are reporting on behalf of other users that are highly similar to them in terms of their mobility, used services and device profiles. Experimental results demonstrate the significant gains achieved in terms of the reduced traffic overhead and overall consumed users’ devices power while achieving a high monitoring accuracy, adaptation time speedup, base station load balancing, and individual providers’ payoffs.

The main goal of today’s wireless Service Providers (SPs) is to provide optimum and ubiquitous service for roaming users while maximizing the SPs own monetary profits. The fundamental objective is to support such requirements by providing solutions that are adaptive to varying conditions in highly mobile and heterogeneous, as well as dynamically changing wireless network infrastructures. This can only be achieved through well-designed management systems. Most techniques fail to utilize the knowledge gained from previously tested reconfiguration strategies on system and network behaviour. This dissertation presents a novel framework that automates the cooperation among a number of wireless SPs facing the challenge of meeting strict service demands for a large number of mobile users. The proposed work employs a novel policy-based system configuration model to automate the process of adapting new network policies. The proposed framework relies on the assistance of a real-time simulator that runs as a constant background process in order to continuously find optimal policy configurations for the SPs’ networks. To minimize the computational time needed to find these configurations, a modified tabu-search scheme is proposed. An objective is to efficiently explore the space of network configurations in order to find optimal network decisions and provide a service performance that adheres to contracted service level agreements. This framework also relies on a distributed Quality of Service (QoS) monitoring scheme. The proposed scheme relies on the efficient identification of candidate QoS monitoring users that can efficiently submit QoS related measurements on behalf of their neighbors. These candidate users are chosen according to their devices’ residual power and transmission capabilities and their estimated remaining service lifetime. Service monitoring users are then selected from these candidates using a novel user-to-user semantic similarity matching algorithm. This step ensures that the monitoring users are reporting on behalf of other users that are highly similar to them in terms of their mobility, used services and device profiles. Experimental results demonstrate the significant gains achieved in terms of the reduced traffic overhead and overall consumed users’ devices power while achieving a high monitoring accuracy, adaptation time speedup, base station load balancing, and individual providers’ payoffs.

Progressive download video services, such as YouTube and podcasts, are responsible for a major part of the transmitted data volume in the Internet and it is expected, that they will also strongly affect mobile networks. Streaming video quality mainly depends on the sustainable throughput achieved during transmission. To ensure acceptable video quality in mobile networks (with limited capacity resources) the perceived quality by the customer (QoE) needs to be monitored by estimation. For that, the streaming video quality needs to be measured and monitored permanently. For TCP based progressive download we propose to extract the the video timestamps which are encoded within the payload of the TCP segments by decoding the video within the payload. The actual estimation is then done by play out buffer fill level calculations based on the TCP segment timestamp and their internal play out timestamp. The perceived quality for the user is derived from the number and duration of video stalls. Algorithms for decoding Flash Video, MP4 and WebM Video have already been implemented. After deriving the play out time it is compared to the timestamp of the respective TCP segment. The result of this comparison is an estimate of the fill level of the play out buffer in terms of play out time within the client. This estimation is done without access to the end device. The same measurement procedure can be applied for any TCP based progressive download Internet service. Video was simply taken as an example because of its current large share in traffic volume in operator networks.

Progressive download video services, such as YouTube, are responsible for a major part of the transmitted data volume in the Internet and it is expected, that they also will strongly affect mobile networks. Streaming video quality mainly depends on the sustainable throughput achieved during transmission. In order to achieve an acceptable video quality in mobile networks (with limited capacity resources), traffic engineering mechanisms have to be applied. For that, the streaming video quality needs to be measured and monitored permanently. Therefore, the video timestamps which are encoded within the payload of the TCP segments have to be extracted. For that it is necessary to decode the video within the transported payload. Algorithms for decoding Flash Video, MP4 and WebM Video have already been implemented as a demonstration implementation in support of the network based measurement contribution to SG12 by Chemnitz University for TCP encoded progressive download Internet services. In the demonstration, the derived play out buffering from the monitored traffic is being output internally. A second application is then used to graphically display the estimation result. The measurement and estimation is solely done within a measurement point of an operator network without access to the client’s end device.