Proposed an Adaptive Bitrate Algorithm based on Measuring Bandwidth and Video Buffer Occupancy for Providing Smoothly Video Streaming
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 2
Abstract
Dynamic adaptive streaming via HTTP (DASH) has been popular disseminated over the Internet especially under the circumstances of the time varying network, which it is currently the most challenging for providing smoothly video streaming via high quality. In DASH system, after completing the download of one segment, the player estimates the available network bandwidth by calculating the downloading throughput and then adapting the video bitrate level based on its estimations. However, the estimated bandwidth in the application layer is not accurate due to off-intervals appearance during the downloading process. To avoid the unfairness of bandwidth estimation by the clients, this work proposes a logarithmic approach for received network bandwidth, which includes increasing or decreasing this bandwidth logarithmically to converge the fair share bandwidth (estimated bandwidth). After obtaining the measured bandwidth, an adaptive bitrate algorithm is proposed by considering this measured bandwidth in addition to video buffer occupancy. The video buffer model is associated with three thresholds (i.e. one for initial startup and two for operating thresholds). When the video buffer’s level stays between the two operating thresholds, the video bitrate will keep unchanged. Otherwise, when the buffer occupancy is too high or too low, an appropriate video bitrate is chosen to avoid buffer overflow/underflow. Simulation results show that the proposed scheme is able to converge the measured bandwidth to the fair share bandwidth very quickly. Also the proposed scheme is compared with conventional scheme, we found that our proposed scheme outperforms in achieving the best performance in terms of efficiency, stability and fairness.
Authors and Affiliations
Saba Qasim Jabbar, Dheyaa Jasim Kadhim, Yu Li
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