Modeling Dynamic Adaptive Streaming Over Information-Centric Networking
The dynamic adaptive streaming technique flexibly adapts the video bit-rate to link fluctuations, which can improve the quality of experience (QoE). In this paper, we present a systematic framework of video streaming in the context of information-centric networking, in order to facilitate the large-...
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2016-01-01
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doaj-f1496ae82d3f4b4d8ad0059c178521fe2021-03-29T19:44:34ZengIEEEIEEE Access2169-35362016-01-0148362837410.1109/ACCESS.2016.26211147742320Modeling Dynamic Adaptive Streaming Over Information-Centric NetworkingRiheng Jia0Zhe Liu1Xiong Wang2Xiaoying Gan3Xinbing Wang4Jun Jim Xu5Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, ChinaState Key Laboratory of Integrated Services Networks, Xidian University, Xi’an, ChinaDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, ChinaDepartment of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, ChinaCollege of Computing, Georgia Institute of Technology, Atlanta, GA, USAThe dynamic adaptive streaming technique flexibly adapts the video bit-rate to link fluctuations, which can improve the quality of experience (QoE). In this paper, we present a systematic framework of video streaming in the context of information-centric networking, in order to facilitate the large-scale deployment of the dynamic adaptive streaming technique. Specifically, we design the network as a two-layer coordinating structure, namely, the control layer and the transmission layer. The control layer employs the statistical data recorders to record the variations of the video popularity, link states, and user demands. On the other side, the network forwards user requests and caches data packets in the transmission layer, based on the statistical data which is obtained in the control layer. In addition, the network executes the real-time monitoring of link conditions in the transmission layer, and adjusts the video bit-rate accordingly. Under the above feedback circumstance, we first develop a distributed algorithm of joint dynamic forwarding and caching to theoretically maximize the total user demand rates within the network stability region. Then, we modify the distributed algorithm with a practical caching strategy to make the system applicable to real scenarios. Simulation results show the superior performance of the modified distributed algorithm in terms of low user delay and high QoE performance.https://ieeexplore.ieee.org/document/7742320/Information-centric networking (ICN)video streamingdistributed algorithm |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Riheng Jia Zhe Liu Xiong Wang Xiaoying Gan Xinbing Wang Jun Jim Xu |
spellingShingle |
Riheng Jia Zhe Liu Xiong Wang Xiaoying Gan Xinbing Wang Jun Jim Xu Modeling Dynamic Adaptive Streaming Over Information-Centric Networking IEEE Access Information-centric networking (ICN) video streaming distributed algorithm |
author_facet |
Riheng Jia Zhe Liu Xiong Wang Xiaoying Gan Xinbing Wang Jun Jim Xu |
author_sort |
Riheng Jia |
title |
Modeling Dynamic Adaptive Streaming Over Information-Centric Networking |
title_short |
Modeling Dynamic Adaptive Streaming Over Information-Centric Networking |
title_full |
Modeling Dynamic Adaptive Streaming Over Information-Centric Networking |
title_fullStr |
Modeling Dynamic Adaptive Streaming Over Information-Centric Networking |
title_full_unstemmed |
Modeling Dynamic Adaptive Streaming Over Information-Centric Networking |
title_sort |
modeling dynamic adaptive streaming over information-centric networking |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2016-01-01 |
description |
The dynamic adaptive streaming technique flexibly adapts the video bit-rate to link fluctuations, which can improve the quality of experience (QoE). In this paper, we present a systematic framework of video streaming in the context of information-centric networking, in order to facilitate the large-scale deployment of the dynamic adaptive streaming technique. Specifically, we design the network as a two-layer coordinating structure, namely, the control layer and the transmission layer. The control layer employs the statistical data recorders to record the variations of the video popularity, link states, and user demands. On the other side, the network forwards user requests and caches data packets in the transmission layer, based on the statistical data which is obtained in the control layer. In addition, the network executes the real-time monitoring of link conditions in the transmission layer, and adjusts the video bit-rate accordingly. Under the above feedback circumstance, we first develop a distributed algorithm of joint dynamic forwarding and caching to theoretically maximize the total user demand rates within the network stability region. Then, we modify the distributed algorithm with a practical caching strategy to make the system applicable to real scenarios. Simulation results show the superior performance of the modified distributed algorithm in terms of low user delay and high QoE performance. |
topic |
Information-centric networking (ICN) video streaming distributed algorithm |
url |
https://ieeexplore.ieee.org/document/7742320/ |
work_keys_str_mv |
AT rihengjia modelingdynamicadaptivestreamingoverinformationcentricnetworking AT zheliu modelingdynamicadaptivestreamingoverinformationcentricnetworking AT xiongwang modelingdynamicadaptivestreamingoverinformationcentricnetworking AT xiaoyinggan modelingdynamicadaptivestreamingoverinformationcentricnetworking AT xinbingwang modelingdynamicadaptivestreamingoverinformationcentricnetworking AT junjimxu modelingdynamicadaptivestreamingoverinformationcentricnetworking |
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1724195783326040064 |