2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
Seohyang Kim , Department of Computer Science Engineering, Seoul National University, Gwanak-ro, Republic of Korea
Hayoung Oh , Department of Electrical and Information Engineering, Soongsil University, Sangdo-ro, Seoul, Republic of Korea
Chongkwon Kim , Department of Computer Science Engineering, Seoul National University, Gwanak-ro, Republic of Korea
To optimize resource usage in communication, various algorithms have been proposed to enhance efficiency in data, energy, and throughput. Here we propose a data utility cognitive green video streaming strategy with novel streaming-chunk scheduling algorithm to balance data efficiency and energy efficiency. We focused on maximizing the ratio of data utility to electricity. Simulation study results showed that our proposed strategy could reduce 10?70% data waste while consuming almost the same amount of energy compared to the latest and the most efficient solution considering both data and energy. In addition, it increased the ratio of data utility to electricity. Since our strategy uses simple algorithm with a divide and conquer approach avoiding too many multiplication operators, it requires only 14 percent of calculation time compared to the currently available best strategy.
Streaming media, Partitioning algorithms, IEEE 802.11 Standard, Indexes, Mathematical model, Energy consumption, Electronic mail
Seohyang Kim, Hayoung Oh and Chongkwon Kim, "Data utility cognitive green video streaming," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 317-320.