Abstract-Viewers using HTTP Adaptive Streaming (HAS) without sufficient bandwidth undergo frequent quality switches that hinder their watching experience. This situation, known as instability, is produced when HAS players are unable to accurately estimate the available bandwidth. Moreover, when several players stream over a bottleneck link, their individual adaptation techniques may result in an unfair share of the channel. These are two detrimental issues in HAS technology, which is otherwise very attractive. To overcome them, a group of solutions are proposed in the literature that can be classified as network-assisted HAS. Solving stability and fairness only in the player is difficult, because a player has a limited view of the network. Using information from network devices can help players in making better adaptation decisions. The contribution of this paper is three-fold. First, we describe our implementation in the form of an HTTP proxy server, and show that both stability and fairness are strongly improved. Second, we present an analytical model that allows to compute the number of changes in video quality and the bitrate of a video stream. Third, we validate the accuracy of the model by comparing the modelbased estimations for the number of changes in video quality and for the mean bitrate of a video stream, with results in a real implementation of our HAS assistant. The results show that the model-based results are highly accurate. As such, this model is useful in practice for planning video delivery networks that use in-network HAS assistants, and enables us to analyze the stability and the mean bitrate of HAS streams prior to real deployment.

The future of communications will be driven by mobility, and specifically innovations on three fronts: Network and Cloud, Integrated Software and Devices and User Interfaces. The cumulative experience of advances in these areas will weave mobile communications even more tightly into the fabric of our global economy and our daily lives. John Donovan will show applications and devices that AT&T is working on today in its labs and innovation centers that illustrate the industry developments it's helping to drive. He'll close by sharing his vision for where these developments will take the enterprise and consumer mobile broadband experience in 2020 and beyond. Biography: Mr. Donovan is chief technology officer for AT&T. In this role, he oversees the company's global technology direction and innovation road map, including product development, network and engineering operations, AT&T Labs and the security and intellectual property organizations. Mr. Donovan previously was executive vice president of product, sales, marketing and operations at Verisign Inc., a technology company that provides Internet infrastructure services. At VeriSign, Mr. Donovan was responsible for leading VeriSign's global sales organization, driving the expansion of broad solutions offerings, and integrating a global professional services capability. Before that, he was chairman and CEO of inCode Telecom Group Inc., where he helped shape strategic direction and positioning for wireless network operators around the globe. Previously, Mr. Donovan was a partner with Deloitte Consulting, where he was the Americas Industry Practice director for telecom. He is chairman of the board of the Alliance for Telecommunications Industry Solutions (ATIS), and is a director on the board of The Wholesale Applications Community (WAC).

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Using Mobile Ad-hoc Networks (MANETs) for audio and video transmission is very promising for application domains such as emergency and rescue. However, audio/video streaming services are not designed for such dynamic and unstable networks. The problems are even more important in so-called sparse MANETs where the node density is relatively low so that disconnections and network partitions are common. We have designed an architecture that combines MANET routing with caching and delay tolerant store-carry-forward operations in an overlay network to improve the quality of audio/video transmission over sparse MANETs. We have implemented a prototype to evaluate the architecture. The results from the experiments demonstrate that our system clearly outperforms simple client-server solutions when the network has temporal disconnections.

Dynamic adaptive streaming over HTTP (DASH) is a simple, but effective, technology for video streaming over the Internet. It provides adaptive streaming while being highly scalable at the side of the content providers. However, the mismatch between TCP and the adaptive bursty nature of DASH traffic results in underperformance of DASH streams in busy networks. This paper describes a networking architecture based on the Software Defined Networking (SDN) paradigm. Controllers in the network with a broad overview on the network activity provide two mechanisms for adaptation assistance: explicitly signaling target bitrates to DASH players and dynamic traffic control in the network. We evaluate how each of these mechanisms can contribute to the delivery of a stable and high quality stream. It shows that our architecture improves the quality of experience by doubling the video bitrate and reducing disturbing quality switches. As such, this paper contributes insights on how to implement DASH-aware networking that also enables internet service providers, network administrators, and end-users to configure their networks to their requirements.

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Scalable streaming has emerged as a feasible solution to resolve users' heterogeneity problems. Scalable Video Streaming (SVC) is the technology that has served as the definitive impulse for the growth of streaming adaptive systems. Systems need to incorporate user's Quality of Experience (QoE) in the layer switching algorithms. This paper characterizes the relation between human decisions and objective metrics that could be mapped into systems. We have performed extensive subjective experiments to corroborate the preference towards adaptive systems when compared to traditional non-adaptive systems. The resulting subjective scores are correlated with most relevant Full Reference (FR) objective metrics. We obtain an exponential relationship between human decisions and the same decisions expressed as a difference of objective metrics. A strong correlation with subjective scores validates objective metrics to be used as aid in the adaptive decision taking algorithms to improve overall systems performance. Results show that, among the evaluated objective metrics, PSNR is the metric that provide worse results in terms of reproducing the human decisions.