QoE routing of video streaming over manet by using NS3, VLC, LXC

Abstract EN

As technology is advancing the demand on video streaming which is one of the technologies increasing every day.

From Through the last years, numerous researchers have presented various techniques and several algorithms for correct and dependable video streaming.

In this paper, proposed system methods are introduced based on the described model of the network.

The main flowchart is presented for the working procedure of the considered MANET to simulate video streaming between the nodes through VLC media player.

In addition, it extraction the features of video and the original quality of experience (QoE) take it from an international dataset.

Then describing and applying the neural network algorithm to training all videos in dataset.

Finally test the video in this neural network and finding new QoE, the experiment begins with entering video and then streaming it by the VLC and sending it throw the MANET network, the video received by another VLC.

The received videos are stored in the VLC and then the features extraction is applied for all the received videos.

These features are used as an input in a neural network which gives result as new QoE videos scores.

The results of new QoE score are compared with the original quality of experience score which is found in the dataset.

The best qualified video is the one that is close to the original quality of experience video.

two examples are applied in this paper that shows the aim of the system.

The suggested system achieves 6 % average error and succeeded in transforming video with high average quality up to 94%.