Scalable Virtual Network Video-Optimizer for Adaptive Real-Time Video Transmission in 5G Networks

Abstract

The increasing popularity of video applications and ever-growing high-quality video transmissions (e.g. 4K resolutions), has encouraged other sectors to explore the growth of opportunities. In the case of health sector, mobile Health services are becoming increasingly relevant in real-time emergency video communication scenarios where a remote medical experts' support is paramount to a successful and early disease diagnosis. To minimize the negative effects that could affect critical services in a heavily loaded network, it is essential for 5G video providers to deploy highly scalable and priorizable in-network video optimization schemes to meet the expectations of a large quantity of video treatments. This paper presents a novel 5G Video Optimizer Virtual Network Function (vOptimizerVNF) that leverages the latest technologies in 5G and video processing to address this important challenge. Advanced traffic filtering is coupled with Scalable H.265 video coding to enable run-time bandwidth-saving video optimization without compromising Quality of Service (QoS); kernel-space video processing is introduced to achieve further performance gains; and the use of a Virtual Network Function (VNF) facilitates dynamic deployment of virtualized video optimizers to achieve scalability and flexibility in this service. The proposed approach is implemented in a realistic 5G testbed and empirical results demonstrate the superior scalability and performance achieved.

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