Real-time data flow models and congestion management for wire and wireless IP networks

Includes abstract.Includes bibliographical references (leaves 103-111).In video streaming, network congestion compromises the video throughput performance and impairs its perceptual quality and may interrupt the display. Congestion control may take the form of rate adjustment through mechanisms by attempt to minimize the probability of congestion by adjusting the rate of the streaming video to match the available capacity of the network. This can be achieved either by adapting the quantization parameter of the video encoder or by varying the rate through a scalable video technique. This thesis proposes a congestion control protocol for streaming video where an interaction between the video source and the receiver is essential to monitor the network state. The protocol consists of adjusting the video transmission rate at the encoder whenever a change in the network conditions is observed and reported back to the sender

Quality-oriented adaptation scheme for multimedia streaming in local broadband multi-service IP networks

The research reported in this thesis proposes, designs and tests the Quality-Oriented Adaptation Scheme (QOAS), an application-level adaptive scheme that offers high quality multimedia services to home residences and business premises via local broadband IP-networks in the presence of other traffic of different types. QOAS uses a novel client-located grading scheme that maps some network-related parameters’ values, variations and variation patterns (e.g. delay, jitter, loss rate) to application-level scores that describe the quality of delivery. This grading scheme also involves an objective metric that estimates the end-user perceived quality, increasing its effectiveness. A server-located arbiter takes content and rate adaptation decisions based on these quality scores, which is the only information sent via feedback by the clients. QOAS has been modelled, implemented and tested through simulations and an instantiation of it has been realized in a prototype system. The performance was assessed in terms of estimated end-user perceived quality, network utilisation, loss rate and number of customers served by a fixed infrastructure. The influence of variations in the parameters used by QOAS and of the networkrelated characteristics was studied. The scheme’s adaptive reaction was tested with background traffic of different type, size and variation patterns and in the presence of concurrent multimedia streaming processes subject to user-interactions. The results show that the performance of QOAS was very close to that of an ideal adaptive scheme. In comparison with other adaptive schemes QOAS allows for a significant increase in the number of simultaneous users while maintaining a good end-user perceived quality. These results are verified by a set of subjective tests that have been performed on viewers using a prototype system

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