# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 5 Discussion and Data Analysis  
### Experiment Plan and Data Description  
In this experiment, we used the DASHIF reference player web streaming application for adaptive streaming to users. This was to collect stream properties and understand user response based on quantitative research. Firstly the streaming server streamed selected videos in fixed properties such as a collection of fixed frame-rates, and resolutions based on chosen video segmentation. Fixed network properties controlled the experiment from a network backend perspective; On the web form, there was a MOS rating where the user shared his/her experience. The quantitative questionnaires were limited to a rating from 1 to 5 to reflect the relevant chosen video. All network data was captured from the user, including the questionnaire, screen properties and stream. In addition to server-side streaming properties, data recorded was placed in the processing phase. Then, concluded based on highly impacting features whether a user was satisfied or not. The experiment's expectation was to outline the correct parameters (such as the manipulation of resolution) that will be used in the next experiment as an editable user choice configuration. A realistic video MPD dataset was generated based on segment collection. This dataset represented a group of MPD manifests and m4s, encoded to run on a DASHIF reference player for dynamic adaptive streaming testing. We then extracted from the manifests seven video  
Figure 3: System test bed process and QoE evaluation. features that were our main input to the data training. These included initial buffer length, live buffer length, bit rate downloading, dropped frames, latency, and video resolution with indexing information and three MOS ratings made by every user while they were streaming as shown in Table 6. These features were selected as they are the primary factors that are affected by network performance which can affect the user's experience; they were all used along with multiple other network monitoring properties and were chosen due to their direct effect on the prediction score. These features along with the network features themselves aided the classifications algorithms to learn and identify the patterns of user satisfaction. Furthermore, our training data consisted of multiple properties that made them precise and unique. For the lowest network limitations that were implemented, the downloading bit rates ranged from 45,373 to 88,482 bps with a noticeable initial loading delay that averaged out to be around 2.4 s. Medium networking runs provided a better range of downloading bit rate, averaging around 317,328 bps and 1.58 s of initial load delay. Finally, when the network was given a bit more space and room to work, the data showed an approximate bit rate download of 2,147,880 bps and 1.3 s of initial loading delay. This is expressed in Table 7 for a clearer view. It is also important to know that the test bed was engineered to monitor the effects of network congestion on the quality of experience. This study shows the process of training the output data first to understand and generate a predicted MOS based on the trained model, increasing the ease of analysis of applications and tests that require MOS prediction within the test bed without the need for more human ratings. Figures 4-7 show the bit rate downloading, resolution, dropped frames, and buffer length. Figure 8 shows the vMOS of the users on a 3D scatter plot where the X-Axis shows the time frame, the Y-Axis shows the users who participated, and the Z-Axis shows the MOS ratings. This figure explains the number of recorded responses, and we can see the direct relation of the network features to the MOS and the time frame of uploading. It seems that the data present a logical front of an increase in performance that directly leads to an increase in vMOS for the quality preserved by the users.