Multi-Resolution Multimedia QoE Models for IPTV Applications

Internet television (IPTV) is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to pro-actively deliver optimum user Quality of Experience (QoE) for IPTV, service providers need to identify network bottlenecks in real-time. In this paper, we develop psycho-acousticvisual models that can predict user QoE of multimedia applications in real-time based on online network status measurements. Our models are neural network-based and 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, our models account for jitter and loss levels, as well as router queuing disciplines: packetordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks.