In-network adaptation of SHVC video in software-defined networks

Awobuluyi, Olatunde; Nightingale, James; Wang, Qi; Calero, José M. Alcaraz; Grecos, Christos

doi:10.1117/12.2227550

Cited by 2 publications

(1 citation statement)

References 18 publications

(21 reference statements)

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“…In Reference [31], a traffic engineering method has been proposed to feature the video adaptation. Here, a study has been carried to understand the importance of SDN for video streaming.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Adaptation Parameters for Dynamic Video Adaptation in Wireless Network Using Experimental Method

et al. 2021

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A wireless network gives flexibility to the user in terms of mobility that attracts the user to use wireless communication more. The video communication in the wireless network experiences Quality of Services (QoS) and Quality of Experience (QoE) issues due to network dynamics. The parameters, such as node mobility, routing protocols, and distance between the nodes, play a major role in the quality of video communication. Scalable Video Coding (SVC) is an extension to H.264 Advanced Video Coding (AVC), allows partial removal of layers, and generates a valid adapted bit-stream. This adaptation feature enables the streaming of video data over a wireless network to meet the availability of the resources. The video adaptation is a dynamic process and requires prior knowledge to decide the adaptation parameter for extraction of the video levels. This research work aims at building the adaptation parameters that are required by the adaptation engines, such as Media Aware Network Elements (MANE), to perform adaptation on-the-fly. The prior knowledge improves the performances of the adaptation engines and gives the improved quality of the video communication. The unique feature of this work is that, here, we used an experimental evaluation method to identify the video levels that are suitable for a given network condition. In this paper, we estimated the adaptation parameters for streaming scalable video over the wireless network using the experimental method. The adaptation parameters are derived using node mobility, link bandwidth, and motion level of video sequences as deciding parameters. The experimentation is carried on the OMNeT++ tool, and Joint Scalable Video Module (JSVM) is used to encode and decode the scalable video data.

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“…In Reference [31], a traffic engineering method has been proposed to feature the video adaptation. Here, a study has been carried to understand the importance of SDN for video streaming.…”

Section: Related Workmentioning

confidence: 99%