# Multi-Layer QoE Learning System Implemented by Fiberhost
## 3 Multi-Layer QoE Metric Nature
### Iv Service Layer
Across the 41 aggregation devices distributed throughout the country, the results show that the percentage number of stalls with respect to all connected devices in a given location oscillated between 20 and 33%, which is a similar value for all aggregation routers. The errors appeared in the entire TV infrastructure, which means that the set-top boxes were not oriented to specific streaming servers as regards the location. With this approach, it can be expected that customers throughout the area will report an outage through a support line. However, from the QoE and CX (Customer eXperience--as described in Section 4) assessment perspective, the percentage of decoders experiencing a problem may not be a suitable metric due to the fact that for the location covered by the Aggregation-12 device, the possible number of customers who would be willing to report a problem is 1357, whereas for Aggregation-17, the number is only 65. The corresponding figures for both locations are 28.88% and 28.26% of devices with problems. Nevertheless, in the former case, the operator must expect that the queue of customers who would wish to report a problem to the inbound hotline will be much larger than in the latter case. This is significant information for the AX parameter (Analog eXperience--as described in Section 4), which would affect the level of QoE associated with the helpline calls. Figure 5: Percentage of played intermediate streams. Figure 6 presents the geographic distribution of stalls in a percentage manner. Figure 6a presents the moment when two CDN servers experienced an outage. Figure 6b shows the total number of stalls from another day between 6 and 7 pm, which is considered the beginning of prime time. In this case, the number of errors was small and the stalls occurred in isolated instances, mainly due to wireless network problems at the STB connection site. Because of the percentage distribution, it is difficult in this case, from the operator's perspective, to plan the resources related to the incoming line for the area or the number of technicians who could help customers solve their problems. These are the primary factors that can affect AX. \begin{table}
\begin{tabular}{l r r r} \hline
**Aggregation Router** & **STBs with Stalls** & **All Devices** & **\%** \\ \hline Aggregation-1 & 26 & 80 & 32.50 \\ Aggregation-2 & 53 & 170 & 31.18 \\ Aggregation-  
The initial delay is proportional to the time required to fill the player buffer above the predefined threshold to start playback. The buffer must be filled after every channel change or usage of additional features such as timeshift, catchup, startover, and others, and finally, after the STB start-up. TV services do not inject ads at the beginning of playback of linear content or VOD content that is part of the service. Because of this, and because it is less important than stalls and changes in the quality of streams, the initial delay will not be considered in this article. Stalls and bitrate changes of video streams are the most important factors affecting the perception of video quality. User engagement during declining service quality is also significant, and can be expressed by phone calls to a helpline. Given the number of variables that can affect changes in the bitrate, it seems impossible that particular users will not experience bitrate drops in a linear TV system. It is important to catch sufficient data at the local level with respect to individual user and at the global level, i.e., in a given area. Stalls or bitrate changes, which relate to video quality, should be correlated with call center notifications, i.e., the user engagement parameters. Such correlation should occur in real time. Using regression, classification or reinforcement learning methods, it would be possible to generalize the QoE level to the entire user population, due to the fact that it is difficult to obtain an engagement rating from each customer of a TV service. There are a number of works that address the problems of the QoE assessment.