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Journal ArticleDOI

SABR: Network-Assisted Content Distribution for QoE-Driven ABR Video Streaming

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TLDR
T trace-based measurements show the substantial improvement in cache hit rates and QoE metrics in conjunction with SABR indicating a rich design space for jointly optimized SDN-assisted caching architectures for adaptive bitrate video streaming applications.
Abstract
State-of-the-art software-defined wide area networks (SD-WANs) provide the foundation for flexible and highly resilient networking. In this work, we design, implement, and evaluate a novel architecture (denoted as SABR) that leverages the benefits of software-defined networking (SDN) to provide network-assisted adaptive bitrate streaming. With clients retaining full control of their streaming algorithms, we clearly show that by this network assistance, both the clients and the content providers benefit significantly in terms of quality of experience (QoE) and content origin offloading. SABR utilizes information on available bandwidths per link and network cache contents to guide video streaming clients with the goal of improving the viewer’s QoE. In addition, SABR uses SDN capabilities to dynamically program flows to optimize the utilization of content delivery network caches.Backed by our study of SDN-assisted streaming, we discuss the change in the requirements for network-to-player APIs that enables flexible video streaming. We illustrate the difficulty of the problem and the impact of SDN-assisted streaming on QoE metrics using various well-established player algorithms. We evaluate SABR together with state-of-the-art dynamic adaptive streaming over HTTP (DASH) quality adaptation algorithms through a series of experiments performed on a real-world, SDN-enabled testbed network with minimal modifications to an existing DASH client. In addition, we compare the performance of different caching strategies in combination with SABR. Our trace-based measurements show the substantial improvement in cache hit rates and QoE metrics in conjunction with SABR indicating a rich design space for jointly optimized SDN-assisted caching architectures for adaptive bitrate video streaming applications.

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Citations
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Journal ArticleDOI

Stochastic Optimization for Green Multimedia Services in Dense 5G Networks

TL;DR: A novel Stochastic Optimization framework for Green Multimedia Services named SOGMS is proposed herein that targets the maximization of system throughput and the minimization of energy consumption in data delivery.
Proceedings ArticleDOI

Adaptive Video Streaming Using Dynamic NDN Multicast in WLAN

TL;DR: An adaptive video streaming scheme by using the Named Data Networking multicast (named NM-ABR), where NDN multicast groups are dynamically formed according to the network conditions and SVC characteristics, and an SVC-based multiple-layer control approach is proposed to reduce the retransmission of video segments in enhancement layers.
Proceedings ArticleDOI

Full UHD 360-Degree Video Dataset and Modeling of Rate-Distortion Characteristics and Head Movement Navigation

TL;DR: In this paper, the authors investigate the rate-distortion characteristics of full ultra-high definition (UHD) 360° videos and capture corresponding head movement navigation data of virtual reality (VR) headsets.
Proceedings ArticleDOI

Transitions of viewport quality adaptation mechanisms in 360 degree video streaming

TL;DR: This paper presents a 360° video streaming system that transitions between sensor- and content-based predictive mechanisms and shows that the perceived quality can be increased between 50% and 80% compared to systems that only apply either one of the two approaches.
Journal ArticleDOI

SDN Assisted Codec, Path and Quality Selection for HTTP Adaptive Streaming

TL;DR: In this article, the authors proposed a HAS system architecture where Software Defined Networking (SDN) technology is utilized for assisting clients to select the most appropriate video codec and bitrate under the constraint of current network conditions as well as routing the video packet over the appropriate paths.
References
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TL;DR: The second edition of this book is unique in that it focuses on methods for making formal statistical inference from all the models in an a priori set (Multi-Model Inference).
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Proceedings ArticleDOI

A buffer-based approach to rate adaptation: evidence from a large video streaming service

TL;DR: This work suggests an alternative approach: rather than presuming that capacity estimation is required, it is perhaps better to begin by using only the buffer, and then ask whencapacity estimation is needed, which allows us to reduce the rebuffer rate by 10-20% compared to Netflix's then-default ABR algorithm, while delivering a similar average video rate.
Proceedings ArticleDOI

A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP

TL;DR: A principled control-theoretic model is developed that can optimally combine throughput and buffer occupancy information to outperform traditional approaches in bitrate adaptation in client-side players and is presented as a novel model predictive control algorithm.
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