SDNHAS: An SDN-Enabled architecture to optimize qoe in http adaptive streaming

Name: SDNHAS: An SDN-Enabled architecture to optimize qoe in http adaptive streaming
Author: Bentaleb, A., Beğen, Ali Cengiz, Zimmermann, R., Harous, S.

İsim	SDNHAS: An SDN-Enabled architecture to optimize qoe in http adaptive streaming
Yazar	Bentaleb, A., Beğen, Ali Cengiz, Zimmermann, R., Harous, S.
Basım Tarihi:	2017-10
Basım Yeri	- IEEE
Konu	Bitrate adaptation logic, Convex optimization, DASH, fastMPC, HTTP adaptive streaming (HAS), Instability, OpenFlow, Quality of experience (QoE), Reinforcement learning, Scalability, Software defined networking (SDN), Streaming architecture, Unfairness, Underutilization
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	1520-9210
Kayıt Numarası	9229ea9f-a665-4838-8984-0f90d4485964
Lokasyon	Computer Science
Tarih	2017-10
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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.
DOI	10.1109/TMM.2017.2733344
Cilt	19

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SDNHAS: An SDN-Enabled architecture to optimize qoe in http adaptive streaming

Yazar Bentaleb, A., Beğen, Ali Cengiz, Zimmermann, R., Harous, S.

Basım Tarihi 2017-10

Basım Yeri - IEEE

Konu Bitrate adaptation logic, Convex optimization, DASH, fastMPC, HTTP adaptive streaming (HAS), Instability, OpenFlow, Quality of experience (QoE), Reinforcement learning, Scalability, Software defined networking (SDN), Streaming architecture, Unfairness, Underutilization

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 1520-9210

Kayıt Numarası 9229ea9f-a665-4838-8984-0f90d4485964

Lokasyon Computer Science

Tarih 2017-10

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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.

DOI 10.1109/TMM.2017.2733344

Cilt 19