# Multi-Layer QoE Learning System Implemented by Fiberhost
## 6 Plans for Implementation of Supervised Learning  
Figure 10 shows a diagram of the Multi-layer QoE learning system in the TV service, which includes both the AX and DX parts. The AX part is implemented in two ways and involves the collection of labels linked to customer satisfaction with the service. The ratings  
Figure 14: Average ACR grade for entire viewing session for first device. Figure 15: Average ACR grade for entire viewing session for second device. collected from customers are fed into a database of quality ratings, which is accessed by an engine that estimates ratings based on telemetry data. The decoders retrieve the content from CDN servers located behind the load balancer. The choice of a CDN server depends on the rules set on the load balancer and can be based on location, type of content being played, channel and many other operator defined criteria. CDN servers generate their own telemetry logs which are stored in a database for the CDN servers. Decoders located at customer premises record individual content playback actions. They can report on the type of content that is being played, the bit rate of the stream, stalls or the state of the wireless network, among others. A server running the machine learning algorithms queues individual tasks on worker nodes that have access to data collected from the decoders, CDN servers and rating labels. If necessary, the consultant can use the application to check the condition of the installation and service at the customer's home. Based on individual assessments, the system can conclude overall TV platform performance. A supervised machine learning approach would provide a more accurate quality rating than stream clustering. With DX labels that take into account individual feelings of customers, it would be possible to create an algorithm that evaluates not only the video, but also subjective feelings about the video service. The prerequisite for this is that the labels are of high quality. We planned to collect the labels through a short survey about the perceived quality. The data would arrive in the system in two ways. The first will be a simple single-page application with a questionnaire with six questions that will be displayed as a menu item on the decoders Figure 16. The user can answer the questions, which are then sent to the database via an API. A specific user group Friendly User Test (FUT) will be included in the survey. When a customer calls the helpline and reports a problem related to the TV part, a DX survey is collected. The customer is asked a number of questions. These are listed in Table 3 and relate to the questions displayed on the one-page application on STBs. The expected response is a rating on an ACR scale within the range of 1 to 5, where 1 is the most negative feeling and 5 is the most positive feeling. In the first question, we want to find out how the customer feels about the TV service on the day of the survey. The evaluation should cover the entire time the customer watched the content on this particular day. In the second question, we want to discover whether the bitrates of the video streams, mainly the maximum ones, which account for more than 65% of the total traffic on the decoders, have been appropriately adjusted (Figure 5). By inquiring on the preferences of the channels being watched and collecting a sufficient number of ratings, it is possible to deduce what group of channels should have an increased bitrate or a more efficient video coding algorithm applied, which will directly affect the increased QoE level. Figure 16: Application displayed on STBs that contains survey for video quality assessment and label gathering. In questions three, four and five, we want to determine how customers are affected by the previously described problems specific to adaptive streaming. The questions related to stalls, video quality changes and initial delay will allow us to prioritize quality improvements in these areas.