TY - JOUR
T1 - Methods for adaptive video streaming and picture quality assessment to improve QoS/QoE performances
AU - Kanai, Kenji
AU - Wei, Bo
AU - Cheng, Zhengxue
AU - Takeuchi, Masaru
AU - Katto, Jiro
PY - 2019/1/1
Y1 - 2019/1/1
N2 - This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.
AB - This paper introduces recent trends in video streaming and four methods proposed by the authors for video streaming. Video traffic dominates the Internet as seen in current trends, and new visual contents such as UHD and 360-degree movies are being delivered. MPEG-DASH has become popular for adaptive video streaming, and machine learning techniques are being introduced in several parts of video streaming. Along with these research trends, the authors also tried four methods: route navigation, throughput prediction, image quality assessment, and perceptual video streaming. These methods contribute to improving QoS/QoE performance and reducing power consumption and storage size.
KW - Machine learning
KW - MPEG-DASH
KW - Picture quality assessment
KW - Video streaming
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U2 - 10.1587/transcom.2018ANI0003
DO - 10.1587/transcom.2018ANI0003
M3 - Article
AN - SCOPUS:85066941959
VL - E102B
SP - 1240
EP - 1247
JO - IEICE Transactions on Communications
JF - IEICE Transactions on Communications
SN - 0916-8516
IS - 7
ER -