Scalable Robust Implementation of Reliable Streaming (SRIRS) with Tristate aware Quality of Service

In today’s rapidly developing technological world, video streaming plays a vital role in mobile devices where the quality of streaming is significant. Quality of video streaming primarily depends on factors including: device’s features, available bandwidth, and video codec. Existing video streaming techniques do not provide smooth streaming since the streaming quality depends only on estimating the available bandwidth ignoring the device’s static and dynamic features. This research work concentrates on hybrid features of device, i.e. static and dynamic features and also the network features for enhancing video streaming quality. This is achieved by using the following three components (Tristate): Static grade point calculator, energy manager and hybrid bandwidth estimator for estimating quality states respectively. Using the statistical features extracted from the three components, the state machine decision maker with novel ensemble-based adaptive learning algorithm decides on the quality of the video to be streamed. Main objective of this work is to provide scalable, robust video streaming with the services of Tristate components and the development of a novel ensemble-based adaptive learning algorithm to achieve quality of service. This proposed technique is tested with cloud open stack platform under various network conditions like motion, driving and train. The experimental results are compared with those of the existing techniques and the results indicate that the proposed work outperforms other schemes in terms of metrics like PSNR, Bandwidth Estimation, and Smoothness in streaming, Power Consumption Rate, Freeze time and computational time.