Cross-layer optimisation of quality of experience for video traffic
PhD Thesis
Title | Cross-layer optimisation of quality of experience for video traffic |
---|---|
Type | PhD Thesis |
Authors | |
Author | Qadir, Qahhar Muhammad |
Supervisor | Kist, Alexander A. |
Zhang, Zhongwei | |
Institution of Origin | University of Southern Queensland |
Qualification Name | Doctor of Philosophy |
Number of Pages | 180 |
Year | 2016 |
Abstract | Realtime video traffic is currently the dominant network traffic and is set to increase in volume for the foreseeable future. As this traffic is bursty, providing This study introduces a cross-layer QoE-aware optimisation architecture for video traffic. QoE is a measure of the user's perception of the quality of a network service. The architecture addresses the problem of QoE degradation in a bottleneck network. It proposes that video sources at the application layer adapt their rate to the network environment by dynamically controlling their transmitted bit rate. Whereas the edge of the network protects the quality of active video sessions by controlling the acceptance of new sessions through a QoE-aware admission control. In particular, it seeks the most efficient way of accepting new video sessions and adapts sending rates to free up resources for more sessions whilst maintaining As a pathway to the objective, the performance of the video The video's instantaneous aggregate rate was compared to the average aggregate rate which is a calculated rate over a measurement time window. It was found that there is no substantial difference between the two rates except for a small number of video flows, long measurement window, or fast moving contents (such as sport), in which the average is smaller than the instantaneous rate. These scenarios do not always represent the reality. The finding discussed above was the main motivation for proposing a novel video traffic measurement algorithm that is QoE-aware. The algorithm finds the upper limit of the video total rate that can exceed a specific link capacity without the QoE degradation of ongoing video sessions. When implemented in a QoE-aware admission control, the algorithm managed to maintain the QoE for a higher number of video session compared to the calculated rate-based admission controls such as the Internet Engineering Task Force (IETF) standard Pre-Congestion Notification (PCN)-based admission control. Subjective tests were conducted to involve human subjects in rating of the quality of videos delivered with the proposed measurement algorithm. Mechanisms proposed for optimising the QoE of video traffic were surveyed in detail in this dissertation and the challenges of achieving this objective were discussed. Finally, the current rate adaptation capability of video applications was combined with the proposed QoE-aware admission control in a QoE-aware cross-layer architecture. The performance of the proposed architecture was evaluated The algorithms proposed in this study were implemented and evaluated using Network Simulator-version 2 (NS-2), MATLAB, Evalvid and Evalvid-RA. These software tools were selected based on their use in similar studies and availability The proposed architecture will contribute to the preparation for the massive growth of video traffic. The mathematical models of the proposed algorithms contribute to the research community. |
Keywords | video traffic, video, rate estimation, probability, traffic measurement |
ANZSRC Field of Research 2020 | 460609. Networking and communications |
400699. Communications engineering not elsewhere classified | |
400602. Data communications | |
400604. Network engineering | |
Byline Affiliations | School of Mechanical and Electrical Engineering |
https://research.usq.edu.au/item/q4v07/cross-layer-optimisation-of-quality-of-experience-for-video-traffic
Download files
583
total views99
total downloads1
views this month0
downloads this month