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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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","bibtex":"@article{ ISI:000411247600003,\nAuthor = {Bagci, Kadir Tolga and Sahin, Kemal Emrecan and Tekalp, A. 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