Network state Estimation using Raw Video Analysis: vQoS-GAN based non-intrusive Deep Learning Approach
This addresses video quality degradation in streaming services for providers and users, but it is incremental as it applies an existing GAN framework to a specific domain.
The paper tackles the problem of estimating network state parameters like data rate and packet loss from degraded video data in streaming services, achieving over 95% training accuracy with a vQoS-GAN model that also reconstructs the video to its original form.
Content based providers transmits real time complex signal such as video data from one region to another. During this transmission process, the signals usually end up distorted or degraded where the actual information present in the video is lost. This normally happens in the streaming video services applications. Hence there is a need to know the level of degradation that happened in the receiver side. This video degradation can be estimated by network state parameters like data rate and packet loss values. Our proposed solution vQoS GAN (video Quality of Service Generative Adversarial Network) can estimate the network state parameters from the degraded received video data using a deep learning approach of semi supervised generative adversarial network algorithm. A robust and unique design of deep learning network model has been trained with the video data along with data rate and packet loss class labels and achieves over 95 percent of training accuracy. The proposed semi supervised generative adversarial network can additionally reconstruct the degraded video data to its original form for a better end user experience.