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The usage of multimedia applications is growing and the requirements for higher quality content delivery are increasing rapidly in the mobile network environment. The mobile network environment is challenging for recent multimedia applications as compared to the wired network environment in various ways. The multimedia contents are delivered to mobile users over heterogeneous networks. Due to fluctuation in bandwidth and user mobility, the service providers are facing difficulties in providing Quality of Service and guaranteed content delivery for multimedia applications. The existing schemes use the single source and multi-source delivery but are unable to balance between stream quality and network congestion for mobile users. Recently, HTTP is used for video streaming that ensures persistent integration of video quality to current HTTP based servers. The HTTP server stores multiple quality levels to provide adaptive media streaming under varying network environment. We designed a Quality Oriented Multimedia Content Delivery Scheme for the mobile cloud to deliver better quality multimedia contents for the mobile user. A method is developed to measure smooth available bandwidth in varying network condition that is used for bitrate selection. An optimized rate adaptation algorithm is designed and implemented to select the best bitrate for the next video chunk.The multimedia contents are delivered to the mobile device on the basis of the device’s parameters and network environment. A rate adaptation algorithm is designed by using Lyapunov optimization for multisource multimedia content delivery to minimize the video rate switches and provide higher video quality. The mobile cloud based multi-source streaming is designed and implemented to enhance performance in an adaptive video streaming client. The performance is compared with existing streaming techniques to validate the multi-source streaming solution. It considers key factors such as video quality, bitrate level, and quality variations to enhance quality of experience over wireless networks. The objective video quality assessment models like Peak Signal-to-Noise Ratio, Structural Similarity, and Video Quality Measurement are used to measure quality of the video. The client side Quality of Experience metric such as Startup delay, Rebuffering events, and Bitrate switch count are used for evaluation. The results show significant improvement over existing multimedia content delivery schemes.
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