Stochastic Tuning Strategy Based DASH System 


Vol. 43,  No. 10, pp. 1645-1653, Oct.  2018
10.7840/kics.2018.43.10.1645


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  Abstract

In this article, we reinterpret MPEG-DASH (MPEG Dynamic Adaptive Streaming over HTTP, or DASH) media streaming strategy, which is widely adopted by recent media streaming platforms, and transform sequential media requesting episodes utilizing Markov Decision Process (MDP) and propose a media streaming algorithm with stochastic tuning strategy. Firstly, the algorithm designs and decides elements of MDP, which consists of state, action, reward, discount factor, and transition probability. Plus, along with multiple scenarios of video chunk requesting sessions, which are the most essential and realistic data transmission procedure of entire media streaming, we carefully consider many issues such as queue stability, frequency of quality variation, and freezing phenomenon. Also, we design algorithm for client to experience optimal service under possibly drastically varying network bandwidth. In following subsections, we introduce an overview of DASH and elaborate design features of our proposed DASH algorithm. Lastly, we conduct experiments with proposed algorithm and previous MDP-based DASH system related researches by investigating experiments in terms of average quality of video chunk which impacts quality of experience of users.

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  Cite this article

[IEEE Style]

D. Kwon and J. Kim, "Stochastic Tuning Strategy Based DASH System," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 10, pp. 1645-1653, 2018. DOI: 10.7840/kics.2018.43.10.1645.

[ACM Style]

Dohyun Kwon and Joongheon Kim. 2018. Stochastic Tuning Strategy Based DASH System. The Journal of Korean Institute of Communications and Information Sciences, 43, 10, (2018), 1645-1653. DOI: 10.7840/kics.2018.43.10.1645.

[KICS Style]

Dohyun Kwon and Joongheon Kim, "Stochastic Tuning Strategy Based DASH System," The Journal of Korean Institute of Communications and Information Sciences, vol. 43, no. 10, pp. 1645-1653, 10. 2018. (https://doi.org/10.7840/kics.2018.43.10.1645)

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pISSN : 1226 - 4717
eISSN : 2287 - 3880