Abstract:
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 source of live and on-demand video
streaming content. With ever-increasing popularity of online different video streaming services
on heterogeneous platforms, new research challenges are arising day by day. Few of the main
challenges that online video streaming services face are high latency of the video, instability of
the video, unfairness among the clients, inefficiency of the algorithm to adapt to the changes in
the network and the start-up delay of the video. Most of the existing algorithms fail to maintain a
balance between stability and efficiency of the algorithm in unstable network conditions. We
have proposed SHANZ rate adaptation algorithm for which address these challenges. We have
developed two versions of the algorithm. SHANZ-I algorithm works on HTTP1.1 protocol. It is
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, it introduces randomized download
delay for the clients to overcome bandwidth overestimation problem occurred in multiple clients.
The second version we have proposed is SHANZ-II rate adaptation algorithm, which is based on
HTTP/2 protocol. It utilizes HTTP/2 features like server-push, streams multiplexing and header
compression for the enhancement of quality of experience. It minimizes the latency and start-up
delay of the video, which are the main challenges for live video streaming. The algorithm defines
an intelligent control mechanism for server-push, which maximizes the utility function. We have
simulated our algorithm using ns-3 and compared our results with FESTIVE, PANDA and
AAASH algorithms by using multiple test cases. The results demonstrate that our proposed
algorithm outperforms other algorithms by addressing the key issues and by achieving higher
Quality of Experience.
