Conventional to Cloud: Detailed survey and comparative study of multimedia streaming rate Adaptation

Abstract

Infotainment and telecommunication industry is fast evolving towards personalized network connectivity and newer multimedia application services delivered ranging from music playback to ever changing telephony applications. Streaming is most important service, enables the users to view real time multimedia content on-the-go anywhere and everywhere. Quality of service is a major concern in the increasing network traffic and high user demand. Rate adaptation is crucial process which dynamically evaluates, select and control the media rate based on the network deviation, system processing capability and to ensure the best user experience to the consumer. In this paper, the authors conducted comprehensive survey of existing rate adaptation models and algorithms used in conventional, adaptive, cloud assisted streaming methods, lists the important merits, limitations of those algorithms. With an experiment setup, the rate adaptation behavior of each streaming models are evaluated and compared with the other streaming techniques. The analysis shows that adaptive and cloud assisted streaming quickly performs well in adapting to the network variation compare to the conventional streaming models.