Internet television (IPTV) is rapidly gaining popularity and is being widely deployed on the Internet. In order to pro-actively deliver optimum user Quality of Experience (QoE), service providers need to identify network bottlenecks in real-time and assess their impact on the audio and video quality degradation. While doing so, they cannot rely on actual end-users to report their subjective QoE of audio-visual quality. Also, they cannot rely on computationally intensive objective techniques that involve frame-to-frame Peak-Signal-to-Noise Ratio (PSNR) comparisons of original and re-constructed video sequences. In this thesis, psycho-acoustic-visual models that can predict user QoE of multimedia applications in real-time based on online network status measurements are developed and evaluated. These models cater to multi-resolution IPTV applications that include QCIF, QVGA, SD and HD resolutions encoded using popular audio and video codec combinations.
On the network side, the models account for jitter and loss levels, as well as router queuing disciplines: Packet-ordered and Time-ordered first-in-first-out (FIFO). The performance of the multi-resolution multimedia QoE models is evaluated in terms of prediction characteristics, accuracy, consistency and speed. The evaluation results demonstrate that the models are pertinent for: (a) continuous multimedia QoE monitoring, and (b) real- time adaptation of system and network resources, in small-to-large scale deployments of IPTV on the Internet.