Abstract
One of the main challenges in video transmission is understanding and adapting to the varying network bandwidth. The traditional approach of bandwidth estimation is not accurate as there are many factors like congestion that can delay the arrival rate of the ping packet which may lead to a misconception that the bandwidth was low. Thus, the better approach to this problem will be to estimate the link conditions based on the buffer fullness. In this paper, a new system to support streaming of live and stored video through wireless network is proposed which is based on adaptive playback buffer management on the top of HTTP at the client. The buffer fullness is treated as a direct state variable that reflects the fluctuation of the network bandwidth. The buffer fullness estimation predicts the buffer status at a point in the future based on observations of the buffer over a stipulated period of time. The proposed algorithm uses non-linear exponential non-parametric regression for computing the decision parameter. A feedback message is then sent to the server in order to change the quality of the video stream for smoother video play at the client side. The synchronized update and feedback between the server and clients is achieved using HTTP protocol. During the experimentation with live video streaming, the proposed algorithm shows an improvement of 24.48% in average peak signal-to-noise ratio and 6.63% in average structural similarity index against the buffer underflow probability algorithm.
Highlights
The demand for video streaming over a wireless network is rapidly increasing day by day
8 Results and discussion The proposed system uses a non-linear regression algorithm based on exponential non-parametric adaptation (PA) method, which was implemented on Airtel 4G LTE category 4 wireless networks in client-server environment
Another algorithm based on buffer underflow probability (BUP) was implemented to compare with the proposed work
Summary
The demand for video streaming over a wireless network is rapidly increasing day by day. The non-parametric regression techniques provide a suitable mechanism in predicting the state of buffer over a defined interval of time This technique is called recursively for decision-making and implemented in the system loop consisting of client, server, network, and the source of contents. An adaptive layer switching system that uses scalable video coding at the server side and buffer underflow probability on the client side is very efficient for wireless networks that support on demand streaming [8]. The second module monitors the data rate of the stream by packet capture using JPCAP (a Java network packet capture library) This information is used to estimate the buffer fullness at a regular interval. With a view to model the equation on the straight line, from which the slope β and intercept α values can be obtained, and which would provide the “best fit” for the data points, Y is transformed to the non-linear domain given as y 1⁄4 ln A1 þ B1x ð5Þ where
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