Compete or Collaborate: Architectures for Collaborative DASH Video Over Future Networks. Bagci, K. T., Sahin, K. E., & Tekalp, A. M. IEEE TRANSACTIONS ON MULTIMEDIA, 19(10):2152-2165, OCT, 2017.
doi  abstract   bibtex   
Dynamic adaptive streaming over HTTP (DASH) clients compete with each other over one or more bottleneck links in a network, which results in fluctuations in TCP throughput and QoE, QoE unfairness among clients, and underutilization of the network capacity. We propose centralized and distributed architectures for collaboration between network service provider (NSP), video service provider (VSP), and users (DASH clients) to provide NSP-managed or VSP-managed DASH services over software-defined networks (SDN) with quality-of-service (QoS) reserved network slices. We show that QoS reservation alone is not sufficient to overcome QoE fluctuations per client and unfairness between heterogeneous video clients, and clients also need to employ TCP receive-window adaptation knowing their fair-share bitrate. To this effect, we propose two collaborative streaming service models to inform clients about their fair-share bitrates. We first present an NSP-managed service model with centralized collaboration between the NSP, VSP, and the users, where a traffic engineering manager at the NSP assigns a fair-share bitrate to each DASH client. We then present a VSP-managed service model with centralized or distributed collaboration architectures, where in the former the VSP determines the fair-share bitrate for each client over a reserved network slice and in the latter a group of DASH clients sharing a reserved network slice collaborate among themselves. In the novel distributed collaboration framework, collaboration groups are identified by the VSP, and clients within a group share critical parameters with each other so that each client can estimate its fair-share bitrate. Experimental results demonstrate that collaboration rather than competition between clients not only helps them achieve a smooth goodput near their fair-share bitrate, but also improves the total goodput over the reserved slice.
@article{ ISI:000411247600003,
Author = {Bagci, Kadir Tolga and Sahin, Kemal Emrecan and Tekalp, A. Murat},
Title = {{Compete or Collaborate: Architectures for Collaborative DASH Video Over
   Future Networks}},
Journal = {{IEEE TRANSACTIONS ON MULTIMEDIA}},
Year = {{2017}},
Volume = {{19}},
Number = {{10}},
Pages = {{2152-2165}},
Month = {{OCT}},
Abstract = {{Dynamic adaptive streaming over HTTP (DASH) clients compete with each
   other over one or more bottleneck links in a network, which results in
   fluctuations in TCP throughput and QoE, QoE unfairness among clients,
   and underutilization of the network capacity. We propose centralized and
   distributed architectures for collaboration between network service
   provider (NSP), video service provider (VSP), and users (DASH clients)
   to provide NSP-managed or VSP-managed DASH services over
   software-defined networks (SDN) with quality-of-service (QoS) reserved
   network slices. We show that QoS reservation alone is not sufficient to
   overcome QoE fluctuations per client and unfairness between
   heterogeneous video clients, and clients also need to employ TCP
   receive-window adaptation knowing their fair-share bitrate. To this
   effect, we propose two collaborative streaming service models to inform
   clients about their fair-share bitrates. We first present an NSP-managed
   service model with centralized collaboration between the NSP, VSP, and
   the users, where a traffic engineering manager at the NSP assigns a
   fair-share bitrate to each DASH client. We then present a VSP-managed
   service model with centralized or distributed collaboration
   architectures, where in the former the VSP determines the fair-share
   bitrate for each client over a reserved network slice and in the latter
   a group of DASH clients sharing a reserved network slice collaborate
   among themselves. In the novel distributed collaboration framework,
   collaboration groups are identified by the VSP, and clients within a
   group share critical parameters with each other so that each client can
   estimate its fair-share bitrate. Experimental results demonstrate that
   collaboration rather than competition between clients not only helps
   them achieve a smooth goodput near their fair-share bitrate, but also
   improves the total goodput over the reserved slice.}},
DOI = {{10.1109/TMM.2017.2736638}},
ISSN = {{1520-9210}},
EISSN = {{1941-0077}},
Unique-ID = {{ISI:000411247600003}},
}

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