SHANZ Algorithm for QoE Enhancement of HTTP Based Adaptive Video Streaming

In the last decade, there has been an exponential increase in the video traffic over the internet. Social Medias are becoming one of the main sources of live and on-demand video streaming content. With ever-increasing demand of online video streaming services on heterogeneous platforms, new research challenges are arising day by day. Some of the main challenges online video streaming services face are instability of the video, large start-up delay, high video latency, and lesser adaptability of the algorithm. Most of the existing video service providers fail to maintain a balance between stability and efficiency of their algorithm in unstable network conditions. We have proposed a dynamic rate adaptation algorithm with feedback control mechanism and adaptive step up function, which acts as an explicit knob to maintain a balance between stability and efficiency of the algorithm, even in drastic network conditions. Moreover, we have used Randomized download delay to overcome bandwidth overestimation problem occurred in multiple clients. We have simulated our algorithm using ns-3 and compared our results with FESTIVE, PANDA and AAASH algorithms in using multiple test cases. The results demonstrate that our proposed algorithm outperforms other algorithms by achieving higher Quality of Experience.