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Discrete quality assessment in IPTV content distribution networks

Research output: Contribution to Journal/MagazineJournal articlepeer-review

<mark>Journal publication date</mark>08/2011
<mark>Journal</mark>Signal Processing: Image Communication
Issue number7
Number of pages19
Pages (from-to)339-357
Publication StatusPublished
<mark>Original language</mark>English


Maintaining the quality of videos in resource-intensive IPTV services is challenging due to the nature of packet-based content distribution networks (CDN). Network impairments are unpredictable and highly detrimental to the quality of video content. Quality of the end user experience (QoE) has become a critical service differentiator. An efficient real-time quality assessment service in distribution networks is the foundation of service quality monitoring and management. The perceptual impact of individual impairments varies significantly and is influenced by complex impact factors. Without differentiating the impact of quality violation events to the user experience, existing assessment methodologies based on network QoS such as packet loss rate cannot provide adequate supports for the IPTV service assessment. A discrete perceptual impact evaluation quality assessment (DEQA) framework is introduced in this paper. The proposed framework enables a real-time, non-intrusive assessment service by efficiently recognising and assessing individual quality violation events in the IPTV distribution network. The discrete perceptual impacts to a media session are aggregated for the overall user level quality evaluation. With its deployment scheme the DEQA framework also facilitates efficient network diagnosis and QoE management. To realise the key assessment function of the framework and investigate the proposed advanced packet inspection mechanism, we also introduce the dedicated evaluation testbed—the LA2 system. A subjective experiment with data analysis is also presented to demonstrate the development of perceptual impact assessment functions using analytical inference, the tools of the LA2 system, subjective user tests and statistical modelling.