Research on optimization of degree distribution for layer-aligned multipriority rateless codes

• Main Authors: Hung, Lien-En, 洪廉恩
• Other Authors: Hsiao, Hsu-Feng
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/e3j5ew

碩士 === 國立交通大學 === 多媒體工程研究所 === 104 === Data loss or damage are happening frequently in real network environment. A usual solution for this condition is retransmitting the missing data. But this method is not available in specific environment like streaming video or some data have real-time property. Hence, we need to use the data already received to recovery the missing part. Such idea can be done through “Forward error correction” also called “channel coding”, and the rateless codes are famous method in channel coding approaches. An improved coding structure of rateless codes for data having different priorities or streaming of scalable videos is called “Layer-aligned multipriority rateless codes”. This coding method provides controllable protection strengths for different layers of scalable data and the encoding procedure follows the traditional rateless codes with a key element called “degree distribution”, and the degree distribution is the most important component responsible for the efficiency and performance of the rateless codes. In this paper, we are interested in finding better or more suitable degree distribution among layer-aligned rateless codes coding structure with different scenarios. We firstly derived the safety criteria of ripple size developed by a leaping random walk model. Then, we proposed the estimate function for the ripple size variation in layer-aligned multipriority rateless codes. Combining with this two, we formulate the objective optimization problem for degree distribution optimizing. After that, we apply the genetic algorithm to find the optimized degree distribution in layer-aligned multipriority rateless codes with different scenarios. Finally, we compare the performance between the optimized degree distribution and other common degree distributions in terms of data recovery rate.