Presentation on theme: "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,"— Presentation transcript:
1AMES-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. LeungSpeaker:饒展榕
2Outline INTRODUCTION RELATED WORK AMES-CLOUD FRAMEWORK AMOV: ADAPTIVE MOBILE VIDEO STREAMINGESOV: EFFICIENT SOCIAL VIDEO SHARINGIMPLEMENTATION AND EVALUATIONCONCLUSION
3I.INTRODUCTIONRecently there have been many studies on how to improve the service quality of mobile video streaming on two aspects:ScalabilityAdaptability
4Scalability:Mobile video streaming services should support a wide spectrum of mobile devices.Different video resolutionsDifferent computing powersDifferent wireless links (like 3G and LTE)
5Adaptability: author have to adjust the video bit rate adapting to the currently time-varying available link bandwidth of each mobile user.
6AMES-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)
7AMES-Cloud supports distributing video streams efficiently by facilitating a 2-tier structure: First tier is a content delivery networkSecond tier is a data center
8II. RELATED WORK Adaptive Video Streaming Techniques B. Mobile Cloud Computing Techniques
9A. 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 StreamingAdobe and Apple
10B. 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.
14B. 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”.
15Suppose sequence number of current time window is i , the predicted bandwidth can be estimated by:
16C. Matching Between Bandwidth Prediction and SVC Segments
18V. ESOV: EFFICIENT SOCIAL VIDEO SHARING A. Social Content Sharingauthor 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:SubscriptionDirect recommendationPublic sharing
23VII. 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 “non-terminating” video
24Further 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.