TY - JOUR
T1 - SDNHAS
T2 - An SDN-enabled architecture to optimize QoE in HTTP adaptive streaming
AU - Bentaleb, Abdelhak
AU - Begen, Ali C.
AU - Zimmermann, Roger
AU - Harous, Saad
N1 - Funding Information:
Manuscript received February 14, 2017; revised June 7, 2017; accepted July 4, 2017. Date of publication July 28, 2017; date of current version September 15, 2017. This work was supported in part by the National Natural Science Foundation of China under Grant 61472266, in part by the National University of Singapore (Suzhou) Research Institute, and in part by Türk Telekomünikasyon A.S¸. The guest editor coordinating the review of this manuscript and approving it for publication was Dr. Mahbub Hassan. (Corresponding author: Abdelhak Bentaleb.) A. Bentaleb and R. Zimmermann are with the School of Computing, National University of Singapore, Singapore 119077 (e-mail: bentaleb@comp. nus.edu.sg; rogerz@comp.nus.edu.sg).
Publisher Copyright:
© 2017 IEEE.
PY - 2017/10
Y1 - 2017/10
N2 - HTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture called SDNDASH [1] to address HAS scalability issues including video instability, quality of experience (QoE) unfairness, and network resource underutilization, while maximizing per player QoE. While SDNDASH was a significant step forward, there were three unresolved limitations: 1) it did not scale well when the number of HAS players increased; 2) it generated communication overhead; and 3) it did not address client heterogeneity. These limitations could result in suboptimal decisions that led to viewer dissatisfaction. To that effect, we propose an enhanced intelligent streaming architecture, called SDNHAS, which leverages software defined networking (SDN) capabilities of assisting HAS players in making better adaptation decisions. This architecture accommodates large-scale deployments through a cluster-based mechanism, reduces communication overhead between the HAS players and SDN core, and allocates the network resources effectively in the presence of short- and long-term changes in the network.
AB - HTTP adaptive streaming (HAS) is receiving much attention from both industry and academia as it has become the de facto approach to stream media content over the Internet. Recently, we proposed a streaming architecture called SDNDASH [1] to address HAS scalability issues including video instability, quality of experience (QoE) unfairness, and network resource underutilization, while maximizing per player QoE. While SDNDASH was a significant step forward, there were three unresolved limitations: 1) it did not scale well when the number of HAS players increased; 2) it generated communication overhead; and 3) it did not address client heterogeneity. These limitations could result in suboptimal decisions that led to viewer dissatisfaction. To that effect, we propose an enhanced intelligent streaming architecture, called SDNHAS, which leverages software defined networking (SDN) capabilities of assisting HAS players in making better adaptation decisions. This architecture accommodates large-scale deployments through a cluster-based mechanism, reduces communication overhead between the HAS players and SDN core, and allocates the network resources effectively in the presence of short- and long-term changes in the network.
KW - Bitrate adaptation logic
KW - Convex optimization
KW - DASH
KW - FastMPC
KW - HTTP adaptive streaming (HAS)
KW - Instability
KW - OpenFlow
KW - Quality of experience (QoE)
KW - Reinforcement learning
KW - Scalability
KW - Software defined networking (SDN)
KW - Streaming architecture
KW - Underutilization
KW - Unfairness
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U2 - 10.1109/TMM.2017.2733344
DO - 10.1109/TMM.2017.2733344
M3 - Article
AN - SCOPUS:85029151389
SN - 1520-9210
VL - 19
SP - 2136
EP - 2151
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
IS - 10
M1 - 7995089
ER -