Video Delivery Chain Research Articles

Begin with the End in Mind: A Unified End-to-End Quality-of-Experience Monitoring, Optimization, and Management Framework

There has been an increasing consensus in the video distribution industry that the design and operation of the full video delivery chain need to be driven by the quality of experience (QoE) appropriate to the end users. Here, we propose a new framework that uses a unified end-to-end solution to produce consistent QoE scores at all points along the delivery chain under the same evaluation criterion. This is a framework that produces a clear picture instantaneously to operation engineers, managing executives, and content creators about how video QoE degrades along the chain; a framework that allows immediate issue identification, localization, and resolution; a framework that enables quality and resource usage optimization; and a framework that provides reliable predictive metrics for long-term strategic resource and infrastructure allocations. The main challenge in the implementation of such a framework is to create a unified QoE metric that not only accurately predicts human QoE but is also lightweight and versatile, readily plugged into multiple points in the video delivery chain. We show that the SSIMPLUS metric offers the best promise, and we demonstrate the benefits of the proposed framework and QoE metric using bandwidth optimization as an example.

Dynamic Deployment and Optimization of Virtual Content Delivery Networks

Today, over-the-top video streaming is gaining a lot of popularity. In this respect, the virtual content delivery network (CDN) is perceived as a key enabler to circumvent the technical challenges faced by content providers to deliver high-quality content over the Internet. Here, the authors investigate how the two main actors of the video delivery chain--the CDN operator and the ISP--can benefit from network and server virtualization to negotiate dynamic service-level agreements that reduce CDN capital expenditures and operating expenses, while generating more revenue for the ISP. First, the authors present a dataset used to simulate dynamic distributed traffic consumption. Second, they discuss the steps required to deploy and operate a virtual CDN deployed on an ISP's network. Furthermore, they present evaluation results of the proposed solution, based on simple models. Lastly, they elaborate on operational parameters that are used to further optimize the solution. This article is part of a special issue on advancing multimedia distribution.

Dynamic Deployment and Optimization of Virtual Content Delivery Networks

Today, over-the-top video streaming is gaining a lot of popularity. In this respect, the virtual content delivery network (CDN) is perceived as a key enabler to circumvent the technical challenges faced by content providers to deliver high-quality content over the Internet. Here, the authors investigate how the two main actors of the video delivery chain--the CDN operator and the ISP--can benefit from network and server virtualization to negotiate dynamic service-level agreements that reduce CDN capital expenditures and operating expenses, while generating more revenue for the ISP. First, the authors present a dataset used to simulate dynamic distributed traffic consumption. Second, they discuss the steps required to deploy and operate a virtual CDN deployed on an ISP's network. Furthermore, they present evaluation results of the proposed solution, based on simple models. Lastly, they elaborate on operational parameters that are used to further optimize the solution. This article is part of a special issue on advancing multimedia distribution.

No-reference video quality measurement: added value of machine learning

Video quality measurement is an important component in the end-to-end video delivery chain. Video quality is, however, subjective, and thus, there will always be interobserver differences in the subjective opinion about the visual quality of the same video. Despite this, most existing works on objective quality measurement typically focus only on predicting a single score and evaluate their prediction accuracies based on how close it is to the mean opinion scores (or similar average based ratings). Clearly, such an approach ignores the underlying diversities in the subjective scoring process and, as a result, does not allow further analysis on how reliable the objective prediction is in terms of subjective variability. Consequently, the aim of this paper is to analyze this issue and present a machine-learning based solution to address it. We demonstrate the utility of our ideas by considering the practical scenario of video broadcast transmissions with focus on digital terrestrial television (DTT) and proposing a no-reference objective video quality estimator for such application. We conducted meaningful verification studies on different video content (including video clips recorded from real DTT broadcast transmissions) in order to verify the performance of the proposed solution.

Scalable, Wavelet-Based Video: From Server to Hardware-Accelerated Client

Video source, carrier and client diversification have led the video coding community to develop scalable video codecs supporting efficient decoding at varying resolution, frame rate and quality. Scalable video has several advantages over a nonscalable approach, but a large scale deployment is far from trivial and a lot of open questions remain. To resolve these, we developed a complete video delivery chain for scalable wavelet-based video. This includes a video server, a negotiation framework, a video scaling infrastructure and two scalable video clients, one pure software client and one real-time, hardware accelerated client. This paper describes the complete chain and identifies and quantifies the impact of using scalable video in every link of this chain.