The paper describes the architecture and design of an IPTV network monitoring system and some of the use cases it enables. The system is based on distributed agents within IPTV terminal equipment (Set-top box), which collect and send the data to a server where it is analyzed and visualized. In the paper we explore how large amounts of collected data can be utilized for monitoring the quality of service and user experience in real-time, as well as for discovering trends and anomalies over longer periods of time. Furthermore, the data can be enriched using external data sources, providing a deeper understanding of the system by discovering correlations with events outside of the monitored domain. Four supported use cases are described, among them using weather information for explaining away the IPTV quality degradation. The system has been successfully deployed and is in operation at the Slovenian IPTV provider Telekom Slovenije.
Today’s modern home-automation systems and services (HASS) frequently communicate over public telecommunications networks, such as the Internet. Unfortunately, these communication networks do not usually provide sufficient quality (i.e., a predictable delay), which is generally assured in fieldbus HASS networks. Consequently, the user-perceived quality of experience (QoE) cannot be maintained at a satisfactory level when using different HASS devices communicating over an IP-based network. The data transferred over the Internet can experience a non-negligible delay that can have a considerable influence on the QoE. For this reason, the main goal of our research was to measure the influence of the network delay on a subjective QoE assessment, while interacting with some frequently used HASS tasks. The results show that users are satisfied if the delay is kept below 0.8 s, and that they can tolerate delays of over 2 s (depending on the level of the HASS task interactivity). Since such a user-perceived subjective QoE assessment is both time-consuming and expensive we also propose objective QoE assessment models to represent the influence of network delay on a subjective QoE assessment for various HASS tasks.