# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 1 Introduction  
MPEG dynamic adaptive streaming over HTTP (DASH) is an adaptive bit-rate streaming methodology which has many abilities providing the best quality streaming of multimedia-related applications across the Internet, a leap from traditional HTTP web servers. It works based on the criteria of breaking down video content into small sequential data segments, which further work over HTTP. Every segment has a small time duration of playback that consists of multiple characteristics, such as a short movie clip or the time broadcast of an event-like show or sports program. The MPEG-DASH can adapt to alternating network fluctuations and enable the best quality playback with a minimal number of re-buffering occasions. One of the main problems that we are trying to solve with our research is determining the correct classification methods for noisy data such as the output of a DASH media stream. There is a considerable rise in the general quality of experience (QoE) anticipated by users across multimedia distribution strategies such as live streaming of video. Since the number of online client implementations and the equipment usage have increased, there is a significant change in the capability of the end equipment and facilities, such as network bandwidth, which are usually distributed among various such as equipment. Traditionally, best-effort network construction assigns assets depending upon the request of a client and an advanced-level service level agreement (SLA) without taking application and user level necessities into consideration. The such as s are usually unsatisfied due to the perceivable unfairness. The concept of fairness within a network or between network resources can be interrupted in many ways; thus fairness can also be achieved in many different ways depending on the scenario [1]. Some of the gaps that we aimed to fill in this research include (but are not limited to): the possibility to create a hybrid environment of software-defined networks compatible with multiple network controllers and DASHmedia-streaming experimentation; and the creation of an experimentation framework that can enhance the multimedia quality of experience evaluation and prediction for future human-less trials and comparing its outputs, strengths and weakness with the state-of-the-art solution to provide the most advanced noise-free generated data for machine learning predictors. In this paper, a new quality analysis method is used as a distant broadcast technique for measuring the appropriateness of the environment for contributing to multimedia video evaluation. In a laboratory experiment, contributors (past researchers) achieved this quality analysis with various listening devices in various listening surroundings involving a silent room allowing an imitation of a circumstantial noise situation. Their results show important observations of the situation and the attending device on the quality inception, and their physical settings had a direct relation to user perception. Thus, the arrangement of our video trials will be free of sound and will only target one aspect, the quality of the video perceived, in a virtual environment guided by ITU recommendations. We aim to tackle the issues of subjective evaluation of objective QoE models. We proposed an experimentation framework structure through programmable network management for the generation of machine learning (ML) training-ready data and MOS/QoE prediction. We used our test bed's data-generated analysis with real user experimentation and ML for training and predicting QoE based on the generated monitoring data. This way with the generated prediction, limited user testing is needed in the future; hence, future researchers will simply run the generated monitored data from the network tracing tools into the prediction model, and it will generate a predicted MOS for faster and more efficient network level tests and experiments. This model is unique because we tested its data with state-of-the-art ML algorithms and achieved highly accurate prediction results.