AMES-Cloud: A Framework of Adaptive
Mobile Video Streaming and Efficient
Social Video Sharing in the Clouds
作者:Xiaofei Wang, MinChen, Ted
Taekyoung Kwon, LaurenceT. Yang, and
Victor C. M. Leung
Speaker:饒展榕
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Outline
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INTRODUCTION
RELATED WORK
AMES-CLOUD FRAMEWORK
AMOV: ADAPTIVE MOBILE VIDEO STREAMING
ESOV: EFFICIENT SOCIAL VIDEO SHARING
IMPLEMENTATION AND EVALUATION
CONCLUSION
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I.INTRODUCTION
• Recently there have been many studies on
how to improve the service quality of mobile
video streaming on two aspects:
Scalability
Adaptability
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Scalability:
• Mobile video streaming services should
support a wide spectrum of mobile devices.
• Different video resolutions
• Different computing powers
• Different wireless links (like 3G and LTE)
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Adaptability:
author have to adjust the video bit rate
adapting to the currently time-varying
available link bandwidth of each mobile user.
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AMES-Cloud constructs a private agent for each
mobile user in cloud computing environments,
which is used by its two main parts:
• AMoV (adaptive mobile video streaming)
• ESoV (efficient social video sharing)
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AMES-Cloud supports distributing video streams
efficiently by facilitating a 2-tier structure:
• First tier is a content delivery network
• Second tier is a data center
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II. RELATED WORK
A. Adaptive Video Streaming Techniques
B. Mobile Cloud Computing Techniques
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A. Adaptive Video Streaming
Techniques
There are mainly two types of adaptive
streaming techniques, depending on whether
the adaptivity is controlled by the client or the
server.
• Microsoft’s Smooth Streaming
• Adobe and Apple
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B. Mobile Cloud Computing Techniques
• The cloud computing has been well positioned
to provide video streaming services, especially
in the wired Internet because of its scalability
and capability.
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III. AMES-CLOUD FRAMEWORK
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IV. AMOV: ADAPTIVE MOBILE VIDEO
STREAMING
A. SVC(Scalable Video Coding)
B. Adaptability With Monitoring on Link Quality
C. Matching Between Bandwidth Prediction and
SVC Segments
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A. SVC
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B. Adaptability With Monitoring on
Link Quality
• The link quality monitor at mobile client keeps
tracking on metrics including signal strength,
packet round-trip-time (RTT), jitter and packet
loss with a certain duty cycle.
• And the client will periodically report to the
subVC.
• Hereby we define the cycle period for the
reporting as the “time window”.
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• Suppose sequence number of current time
window is i , the predicted bandwidth can be
estimated by:
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C. Matching Between Bandwidth
Prediction and SVC Segments
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V. ESOV: EFFICIENT SOCIAL VIDEO
SHARING
A. Social Content Sharing
author classify the social activities in current
popular SNSs into three kinds, regarding the
impact of the activities and the potential
reacting priority from the point of view of the
recipient:
• Subscription
• Direct recommendation
• Public sharing
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B. Prefetching Levels
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VI. IMPLEMENTATION AND
EVALUATION
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VII. CONCLUSION
• Adaptivemobile video streaming and sharing
framework.
• Efficiently stores videos in the clouds (VC)
• Utilizes cloud computing to construct private
agent (subVC)
• For each mobile user to try to offer “nonterminating” video
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• Further seek to provide “non-buffering”
experience of video streaming.
• The focus of this paper is to verify how cloud
computing can improve the transmission
adaptability and prefetching for mobile users.
• As one important future work, we will carry
out large-scale implementation and with
serious consideration on energy and price cost.
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