Scalable video coding algorithm and rate-distortion optimisation based on cloud computing. (2018)

• Record Type: Journal Article
• Authors: Zhang, Yuejin; Yu, Meng; Hu, Yong
• Is Part Of: International journal of innovative computing and applications. Volume 9:Number 3(2018)
• Page Start: 165
• Page End: 172
• Publication Date: 2018
• Subjects: video distribution network -- VDN -- scalable video coding -- multi-path -- rate-distortion optimisation -- cloud computing; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

In order to provide end users with better quality video, this paper presents an adaptive multipath video stream scalable video coding algorithm, which is based on the cloud computing for H264/AVC extension, with the path of diversity provided by based on the cloud computing video distribution network. The method of using scalable video coding is finally adapted to the various end users. Moreover, it adapts to network bandwidth fluctuation by observing the changes of the available bandwidth over the multiple overlay paths. And performing rate-distortion optimisation in the basis of the end-to-end distortion estimation has given a method of reduce complexity, meanwhile, maintaining the balance of quality and cloud computing security. Experimental results show that the optimisation algorithm based on the cloud computing video distribution network is more effective to estimate network congestion, reduce video packet loss rate and reducing network latency, rate-distortion optimisation performance gain outperform the current redundancy coding scheme and traditional recursive optimal per-pixel estimation based on the optimal macro-block coding mode selection, and thus more effectively ensure the quality of the video network transmission.