부산대학교 도서관

In recent times, the service providers and the operators are facing the problems like visual quality degradations and frequent interruptions during video streaming, due to higher dynamism of network conditions. The video parameters like occupancy, playback quality, underflow/overflow buffer, and rate switching frequency/amplitude affect the quality of user’s experience. In the present scenario, numerous adaptive streaming protocols are developed to provide continuous service to the user’s under complex network condition with heterogeneous devices. In this research article, linear discriminant regression technique is proposed based on cognitive computing in order to predict the user’s quality of experience over software-defined network. By varying the network and the objective parameters, initially the mean opinion score is collected from the users. Further, a new architecture is developed on the basis of linear discriminant regression technique that uses the mean opinion score under different network conditions to predict the expected mean opinion score. The simulation results showed that the linear discriminant regression technique achieved better video quality compared to linear regression technique in terms of peak signal-to-noise ratio, structural similarity index, and video quality metric by varying the video resolutions, bit-rates, and frame rates.