# Quality of Experience Experimentation Prediction Framework Through Programmable Network Management
## 2 Problem Space and Related Work  
HTTP adaptive streaming (HAS), such as MPEG-DASH, splits a broadcast file into many segments. Every segment is encrypted into the number of bit rates to attain access for the user with changing stream specifications. The video segments are consecutively requested by the client, having a maximum bit rate possible, which is approximated based on the network and user needs. The process of adaptive bit rate algorithms (ABR) is the method through which a client suggests the ideal bit rate of the part to download. ABR contains default conditions, but many of them do not reflect the difference between the multiple scenarios that may occur in a production environment and often work poorly when the organization makes changes in the work environment. A recent attempt is to link the increase in the ABR to the capabilities of the interpolation method [5], which is mainly based on a machine learning model. The ML-based ABR strategy is divided into two parts. In the first part, you can adjust the current ABR parameters. A calculation plan based on the ABR variables is proposed. Support systems allow the ABR to change variables depending on the order in which the network conditions are changed. In deep learning, this ABR variable is based on an adaptation policy, where changes are presented as the context of the flow. In deep learning, depending on the strategy used, the parameters of the ABRs, would directly affect the streaming content. Previous research states that bit rate depends upon the ABR models having trained a predictive collection of decisions (SMASH [6]), where a 'combine grouping' scheme was used to make a map of the network-related properties of bit rate. A supervised ML-based ABR was implemented with features related only to the bit rate status. Therefore, there are some restrictions with those predictions as features are focused on limited network factors. Both the conception of engineering and the ML algorithm selections will not be executed in a methodal fashion; moreover, the trained model has planned to support, rather than replace, the existing fixed rules that depend upon modifying the algorithm. Moreover, a recently introduced model-free strategy is Pensieve [7], which uses a reinforcement learning strategy to introduce a neural network based upon the ABR. This strategy makes no explicit assumption related to effective data. Multiple papers have reported issues such as Pensieve; therefore, it is being accompanied by implementing detailed experimental evaluation of Pensieve, keeping various sets of video content and network under observation [8]. In [9] within the training process, the results change significantly when using a web account; the deficit has a high value and does not tend to coincide. They have a high bit rate presented in their video setup. For example, when running UHD and 4K data packets, authors choose reasonably bright screens based on past learning success, indicating that the data are fragmented. A bit rate equal to the brightness percentage is reduced by 50%, the experimental model only learns to achieve maximum results, and the bit rate leads to an inaccessible video level. The heterogeneity of wireless networks means that larger variants continue to be known, and as the value of video resolution continues to grow, so does the popularity of data. This work was therefore encouraged. For research and experimentation, researchers spend an enormous amount of time in the creation of their test environment; most of these environments are quite specific to the target of their research. There are many downloadable virtual environments, such as P4 or SDN-based virtual test beds; however, these environments are very specific to their purpose. Configuring a suitable test bed that has the ability to run multiple solutions from multiple different test beds is very time consuming. Thus, we created a virtual-box environment with all the necessary libraries and applications needed to run P4, Openflow, Python 2 and 3 instances, DASH and Mininet.