# Multi-Layer QoE Learning System Implemented by Fiberhost
## 5 Implementation of Unsupervised Learning
### Results  
The continuous nature of television requires constant evaluation of video quality. The viewer is typically watching linear channels on which video events are presented according to the TV schedule. We divided the quality assessment into levels that included session, channel and individual events on a time scale in order to obtain as much information relevant to video playback as possible. Figure 11 presents the adopted approach. A session is the time from startup to shutdown of the decoder. It can include ratings during playback of different channels, recordings, catchup and startover content. The rating at the channel level is related to the viewing of a single linear channel. At the lowest level is the rating for a single programme, i.e., the events that are displayed in the EPG grid or in the TV programmes that are available on various websites. The rating starts 30 s after the start of playback to avoid channel-hopping, where the customer is looking for a channel that they prefer to watch.  
Figure 11: Timeline for session assessment. The customer can watch many events on a single TV channel. The session begins when the STB power is on, and lasts until shutdown.  
The ratings per channel for two selected decoders are shown in Figures 12 and 13. Figures 14 and 15 show the rating for the whole of the viewing session for the same set of decoders. The ratings are for 24 h. The vertical lines are drawn in each graph to indicate the channel change (Figures 12 and 13) and the start of the viewing session associated with the start of the set (Figures 14 and 15). The decoder shown in Figures 12 and 14 is the reference device, located in the data center. In general, it should always have the best rating but, in this case, especially around the peak traffic hours, i.e., between 7 p.m. and midnight, ratings lower than 5 appeared. This was associated with the degradation of one of the edge servers, and the increased number of degradations appeared on the other decoders. This can also be seen in the numbers associated with the other device. A detection of such degradations is important because such streaming server failures may not be visible on typical monitoring systems, as they may be related to server software issues.  
Figures 13 and 15 show the evaluation for the regular decoders in the households. The time periods between 12:00 and 15:00, 18:00 and 01:00 and after 07:00 are particularly characteristic. It can be seen that there is an increase in the degradation of the various decoders. Between the hours of 12:00 and 15:00, errors appeared on the client devices, whereas this was not observed on the reference device. After 07:00 in the morning, individual errors appeared on the reference unit, but this was much more clearly visible on the client devices. These errors may have been related to maintenance work carried out by an engineer on the CDN server infrastructure. The most problematic period is between 18:00 and 01:00. In this case, significant errors appeared on all devices, and were related to problems in the operator's infrastructure. Identifying this type of degraded connection can help determine whether the failure was global, affecting the entire TV infrastructure, or local, reaching the subscribers' homes. When maintenance work is carried out, it is possible to assess the extent to which viewers are being affected. Searching for individual faulty devices allows for proactive quality improvement on a per-customer basis, which can significantly increase satisfaction with both the content watched by the customer and the service provided by the operator.