alexa User Behaviour Prediction Based Adaptive Mobile Video
ISSN ONLINE(2320-9801) PRINT (2320-9798)

International Journal of Innovative Research in Computer and Communication Engineering
Open Access

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Special Issue Article

User Behaviour Prediction Based Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds

Prabhu R, Gautham K, Nagajothi A
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Abstract

The mobile phones become an essential piece of our daily life, with smart phone sales at the present increased very much and also end-user demands to run many applications have improved. The success of next generation mobile communication depends on the ability of service providers to engineer new added value to video services. The Streams are encoded by the Scalable Video Coding extension of the H.264/AVC standard. Adding or removing the layer is decided based on the user behaviour conditions of the mobile network. The recent advances in the mobile video streams over mobile networks have been souring over these new trends, the wireless link facility not practically support with the growing traffic demand. The gap among the traffic load and the wireless link capacity, along with time-changeable link situations, results reduced quality of mobile video streams over mobile networks, such as extended buffering delays and intermittent disruptions. The new cloud computing technology, we suggest a new framework to get improved quality of video services for the mobile users, which contains of two different parts: A newly distributed web User Behaviour Prediction model is introduce to cognize and predict user behaviour and further Adaptive Policy Pre-fetching and Caching method is addressed for an efficient cloud management. We can apply a new framework model to shows significant improvements in terms of lower loss rate, reduce delay and buffering time. For each mobile user, construct a private agent in the cloud data centre to adjust the mobile video streams flow by the scalable video coding technique based on the response from the mobile user and perform the video prefetching based on the social network analysis. Additionally, the basic tools for providing temporal, spatial, and quality are described in detail and experimentally analyze their complexity and efficiency.

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