Quality of Experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the internet. End-to-end metrics are formed because QoE is dependent on both the users' perception and the service used. Traditionally, network optimization has focused on improving network properties such as QoS. In this paper we examine the Adaptive streaming over a software defined network environment, evaluate and study the media streams, aspects affecting the stream, network and finally analysing the network's features and their direct relationship with the perceived QoE. We then use machine learning to predict future QoE based on network feedback and original user testing. This will help to eliminate future physical experiments and automate the process of predicting QoE.• A Virtual-Box Environment with all the necessary libraries and applications needed to run P4, Openflow, Python 2 and 3 instances, DASH and Mininet. With all essential packages installed, an error-free test

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The NH3 and PH3 molecules achieve differential electron elastic scattering cross-sections for energies ranging from 15 eV to 500 eV for ammonia and from 5 to 500 eV for phosphine. Calculations are performed using partial waveforms describing the target molecule using a single Hartree-Fock Molecular Function Center. The potentials used include a constant part—numerically obtained from quantum computation in addition to subtle effects such as correlation, polarization, and potential exchange,  results of this model clearly show the role of the exchange, as well as the contributions of correlation and polarization, especially at low scattering angles and incident energies. The differential cross-sections obtained were compared with a large amount of experimental data from the research, and good agreement was found. Throughout the scattering angles and power ranges examined here. http://dx.doi.org/10.31257/2018/JKP/2022/140208

Quality of experience (QoE) metrics can be used to assess user perception and satisfaction in data services applications delivered over the Internet. End-to-end metrics are formed because QoE is dependent on both the users’ perception and the service used. Traditionally, network optimization has focused on improving network properties such as the quality of service (QoS). In this paper we examine adaptive streaming over a software-defined network environment. We aimed to evaluate and study the media streams, aspects affecting the stream, and the network. This was undertaken to eventually reach a stage of analysing the network’s features and their direct relationship with the perceived QoE. We then use machine learning to build a prediction model based on subjective user experiments. This will help to eliminate future physical experiments and automate the process of predicting QoE.