# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 3 Preliminaries and Methodology
### DASH Objective Metrics  
The basic remodelling of the automation is the media streaming that includes best quality on demand data and live media content. In the present scenarios, the main interest is to attain the pre-eminent quality of service and experience because of the ever aggregating network consumption and user demand. The traditional type of streaming methodologies confront multiple trials in distributing multimedia content to the end user without \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Database** & **Source** & **Test** & **Encoding** & **Test case** & **HAS-Related Impairments** & **Resolution** \\  & **Videos** & **Videos** & **Configurations** & **Formation** & **Adaeption** \\ \hline LIVEMVQA [10] & 10 & 200 & H.264 at 4 levels & hand-crafted & switching or stalling & No \\ LIVEQHVS [11] & 3 & 15 & H.264 at 21 levels & hand-crafted & switching & No \\ LIVEMSV [12] & 24 & 176 & no compression & hand-crafted & stalling & No \\ Waterloo SQoE-[13] & 20 & 180 & H.264 at 7 levels & hand-crafted & switching & Yes \\ LIVE-Netfile Video QoE & 14 & 112 & H.264 at 6 levels & hand-crafted & initial buffering and stalling and switching & No \\ Database [14] & 14 & 112 & H.264 at 6 levels & hand-crafted & initial buffering and stalling and switching & Yes \\ Waterloo SQoE-III [15] & 20 & 450 & H.264 at 11 levels & simulated & initial buffering and stalling and switching & Yes \\ ITEC DASH [16] & 7 & 131 & H.264 at 6 levels & hand-crafted & initial buffering and stalling and switching & Yes \\ Our Dataset & 6 & 120 & H.264 at 6 levels & simulated & and monitoring & Yes \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of publicly available QoE dataset for HTTP-based adaptive video streaming. lowering the quality of the service. The adaptive HTTP streaming is the ever increasing content-providing tactic which delivers real-time content without negotiating quality and guarantees excellent quality of experience. The selection of bit rate must be effective and durable; it depends upon the nature of the network, and thus we argue that it must always be dynamic. The client potential points towards gaining outstanding quality of service for the end user and achieving an increase in the range of standards and procedures introduced in the adaptive HTTP streaming field. Researching and contrasting is compulsory for executing numerous techniques depending upon predefined merits. The HTTP live streaming, Microsoft smooth streaming and MPEG DASH are rising model techniques of adaptive HTTP streaming. In order to calculate the transporting execution, the experiment moves forward using G-streamer adaptive HTTP streaming. We base our calculations for transporting execution upon changing multiple networks and situations for on-demand streaming and live streaming content. The adaptive HTTP streaming method's result is calculated and examined using pre-explained performance indices. The processed data show that each entertained method of delivery and best conductance is achieved through the predefined advantages. In short, DASH provides appreciable balanced performance throughout multiple network arrangements compared to other streaming approaches. Figure 1 shows the proposed experimental framework for feature evaluation within the test bed. Features, such as network properties and factors that directly affect user QoE, are essential inputs to understanding the user's experience. In this paper, the experiment is staged based on the encoding information in Table 2. The user will watch a video based on the video segmentation and resolution properties mentioned in Table 2; all videos are fixed on 30 frames per seconds on all video streaming qualities, with a minimum buffer time of 2 s of loaded content. The user will watch five 40 s trials of video content on a limited bandwidth of 0.5 mbps, 1 mbps, 3 mbps, 5 mbps and unlimited settings.