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摘要:
In this paper,we investigate video quality enhancement using computation offloading to the mobile cloud computing(MCC)environment.Our objective is to reduce the computational complexity required to covert a low-resolution video to high-resolution video while minimizing computation at the mobile client and additional communication costs.To do so,we propose an energy-efficient computation offloading framework for video streaming services in a MCC over the fifth generation(5G)cellular networks.In the proposed framework,the mobile client offloads the computational burden for the video enhancement to the cloud,which renders the side information needed to enhance video without requiring much computation by the client.The cloud detects edges from the upsampled ultra-high-resolution video(UHD)and then compresses and transmits them as side information with the original low-resolution video(e.g.,full HD).Finally,the mobile client decodes the received content and integrates the SI and original content,which produces a high-quality video.In our extensive simulation experiments,we observed that the amount of computation needed to construct a UHD video in the client is 50%-60% lower than that required to decode UHD video compressed by legacy video encoding algorithms.Moreover,the bandwidth required to transmit a full HD video and its side information is around 70% lower than that required for a normal UHD video.The subjective quality of the enhanced UHD is similar to that of the original UHD video even though the client pays lower communication costs with reduced computing power.
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篇名 Efficient Computation Offloading in Mobile Cloud Computing for Video Streaming Over 5G
来源期刊 计算机、材料和连续体(英文) 学科 工学
关键词 5G video STREAMING CLOUD COMPUTATION OFFLOADING energy efficiency upsampling MOS
年,卷(期) 2019,(8) 所属期刊栏目
研究方向 页码范围 439-463
页数 25页 分类号 TP3
字数 语种
DOI
五维指标
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研究主题发展历程
节点文献
5G
video
STREAMING
CLOUD
COMPUTATION
OFFLOADING
energy
efficiency
upsampling
MOS
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研究分支
研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
计算机、材料和连续体(英文)
月刊
1546-2218
江苏省南京市浦口区东大路2号东大科技园A
出版文献量(篇)
346
总下载数(次)
4
总被引数(次)
0
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