Video coding deep learning-based modeling for long life video streaming over next network generation

Abstract

Availability is one of the primary goals of smart networks, especially, if the network is under heavy video streaming traffic. In this paper, we propose a deep learning based methodology to enhance availability of video streaming systems by developing a prediction model for video streaming quality, required power consumption, and required bandwidth based on video codec parameters. The H.264/AVC codec, which is one of the most popular codecs used in video steaming and conferencing communications, is chosen as a case study in this paper. We model the predicted consumed power, the predicted perceived video quality, and the predicted required bandwidth for the video codec based on video resolution and quantization parameters. We train, validate, and test the developed models through extensive experiments using several video contents. Results show that an accurate model can be built for the needed purpose and the video streaming quality, required power consumption, and required bandwidth can be predicted accurately which can be utilized to enhance network availability in a cooperative environment.

Optimizing Clustering Approaches in Cloud Environments

“…In the past, clusters utilized multiple single-core processors. However, the industry has now introduced chips with multiple processors, or multi-cores, to address the limitations of single-core clusters [31]. A comprehensive understanding of multi-core chips necessitates acknowledging that individual processor cores operate at slower speeds in comparison to single-core processors [32].…”