Proactive Mechanisms for Video-on-Demand Content Delivery

Video delivery over the Internet is the dominant source of network load all over the world. Especially VoD streaming services such as YouTube, Netflix, and Amazon Video have propelled the proliferation of VoD in many peoples' everyday life. VoD allows watching video from a large quantity of content at any time and on a multitude of devices, including smart TVs, laptops, and smartphones. Studies show that many people under the age of 32 grew up with VoD services and have never subscribed to a traditional cable TV service. This shift in video consumption behavior is continuing with an ever-growing number of users. satisfy this large demand, VoD service providers usually rely on CDN, which make VoD streaming scalable by operating a geographically distributed network of several hundreds of thousands of servers. Thereby, they deliver content from locations close to the users, which keeps traffic local and enables a fast playback start. CDN experience heavy utilization during the day and are usually reactive to the user demand, which is not optimal as it leads to expensive over-provisioning, to cope with traffic peaks, and overreacting content eviction that decreases the CDN's performance. However, to sustain future VoD streaming projections with hundreds of millions of users, new approaches are required to increase the content delivery efficiency. To this end, this thesis identifies three key research areas that have the potential to address the future demand for VoD content. Our first contribution is the design of vFetch, a privacy-preserving prefetching mechanism for mobile devices. It focuses explicitly on OTT VoD providers such as YouTube. vFetch learns the user interest towards different content channels and uses these insights to prefetch content on a user terminal. To do so, it continually monitors the user behavior and the device's mobile connectivity pattern, to allow for resource-efficient download scheduling. Thereby, vFetch illustrates how personalized prefetching can reduce the mobile data volume and alleviate mobile networks by offloading peak-hour traffic. Our second contribution focuses on proactive in-network caching. To this end, we present the design of the ProCache mechanism that divides the available cache storage concerning separate content categories. Thus, the available storage is allocated to these divisions based on their contribution to the overall cache efficiency. We propose a general work-flow that emphasizes multiple categories of a mixed content workload in addition to a work-flow tailored for music video content, the dominant traffic source on YouTube. Thereby, ProCache shows how content-awareness can contribute to efficient in-network caching. Our third contribution targets the application of multicast for VoD scenarios. Many users request popular VoD content with only small differences in their playback start time which offers a potential for multicast. Therefore, we present the design of the VoDCast mechanism that leverages this potential to multicast parts of popular VoD content. Thereby, VoDCast illustrates how ISP can collaborate with CDN to coordinate on content that should be delivered by ISP-internal multicast

Quality-aware Content Adaptation in Digital Video Streaming

User-generated video has attracted a lot of attention due to the success of Video Sharing Sites such as YouTube and Online Social Networks. Recently, a shift towards live consumption of these videos is observable. The content is captured and instantly shared over the Internet using smart mobile devices such as smartphones. Large-scale platforms arise such as YouTube.Live, YouNow or Facebook.Live which enable the smartphones of users to livestream to the public. These platforms achieve the distribution of tens of thousands of low resolution videos to remote viewers in parallel. Nonetheless, the providers are not capable to guarantee an efficient collection and distribution of high-quality video streams. As a result, the user experience is often degraded, and the needed infrastructure installments are huge. Efficient methods are required to cope with the increasing demand for these video streams; and an understanding is needed how to capture, process and distribute the videos to guarantee a high-quality experience for viewers. This thesis addresses the quality awareness of user-generated videos by leveraging the concept of content adaptation. Two types of content adaptation, the adaptive video streaming and the video composition, are discussed in this thesis. Then, a novel approach for the given scenario of a live upload from mobile devices, the processing of video streams and their distribution is presented. This thesis demonstrates that content adaptation applied to each step of this scenario, ranging from the upload to the consumption, can significantly improve the quality for the viewer. At the same time, if content adaptation is planned wisely, the data traffic can be reduced while keeping the quality for the viewers high. The first contribution of this thesis is a better understanding of the perceived quality in user-generated video and its influencing factors. Subjective studies are performed to understand what affects the human perception, leading to the first of their kind quality models. Developed quality models are used for the second contribution of this work: novel quality assessment algorithms. A unique attribute of these algorithms is the usage of multiple features from different sensors. Whereas classical video quality assessment algorithms focus on the visual information, the proposed algorithms reduce the runtime by an order of magnitude when using data from other sensors in video capturing devices. Still, the scalability for quality assessment is limited by executing algorithms on a single server. This is solved with the proposed placement and selection component. It allows the distribution of quality assessment tasks to mobile devices and thus increases the scalability of existing approaches by up to 33.71% when using the resources of only 15 mobile devices. These three contributions are required to provide a real-time understanding of the perceived quality of the video streams produced on mobile devices. The upload of video streams is the fourth contribution of this work. It relies on content and mechanism adaptation. The thesis introduces the first prototypically evaluated adaptive video upload protocol (LiViU) which transcodes multiple video representations in real-time and copes with changing network conditions. In addition, a mechanism adaptation is integrated into LiViU to react to changing application scenarios such as streaming high-quality videos to remote viewers or distributing video with a minimal delay to close-by recipients. A second type of content adaptation is discussed in the fifth contribution of this work. An automatic video composition application is presented which enables live composition from multiple user-generated video streams. The proposed application is the first of its kind, allowing the in-time composition of high-quality video streams by inspecting the quality of individual video streams, recording locations and cinematographic rules. As a last contribution, the content-aware adaptive distribution of video streams to mobile devices is introduced by the Video Adaptation Service (VAS). The VAS analyzes the video content streamed to understand which adaptations are most beneficial for a viewer. It maximizes the perceived quality for each video stream individually and at the same time tries to produce as little data traffic as possible - achieving data traffic reduction of more than 80%

Towards Computational Efficiency of Next Generation Multimedia Systems

To address throughput demands of complex applications (like Multimedia), a next-generation system designer needs to co-design and co-optimize the hardware and software layers. Hardware/software knobs must be tuned in synergy to increase the throughput efficiency. This thesis provides such algorithmic and architectural solutions, while considering the new technology challenges (power-cap and memory aging). The goal is to maximize the throughput efficiency, under timing- and hardware-constraints

MediaSync: Handbook on Multimedia Synchronization

This book provides an approachable overview of the most recent advances in the fascinating field of media synchronization (mediasync), gathering contributions from the most representative and influential experts. Understanding the challenges of this field in the current multi-sensory, multi-device, and multi-protocol world is not an easy task. The book revisits the foundations of mediasync, including theoretical frameworks and models, highlights ongoing research efforts, like hybrid broadband broadcast (HBB) delivery and users' perception modeling (i.e., Quality of Experience or QoE), and paves the way for the future (e.g., towards the deployment of multi-sensory and ultra-realistic experiences). Although many advances around mediasync have been devised and deployed, this area of research is getting renewed attention to overcome remaining challenges in the next-generation (heterogeneous and ubiquitous) media ecosystem. Given the significant advances in this research area, its current relevance and the multiple disciplines it involves, the availability of a reference book on mediasync becomes necessary. This book fills the gap in this context. In particular, it addresses key aspects and reviews the most relevant contributions within the mediasync research space, from different perspectives. Mediasync: Handbook on Multimedia Synchronization is the perfect companion for scholars and practitioners that want to acquire strong knowledge about this research area, and also approach the challenges behind ensuring the best mediated experiences, by providing the adequate synchronization between the media elements that constitute these experiences

Analyzing Granger causality in climate data with time series classification methods

Attribution studies in climate science aim for scientifically ascertaining the influence of climatic variations on natural or anthropogenic factors. Many of those studies adopt the concept of Granger causality to infer statistical cause-effect relationships, while utilizing traditional autoregressive models. In this article, we investigate the potential of state-of-the-art time series classification techniques to enhance causal inference in climate science. We conduct a comparative experimental study of different types of algorithms on a large test suite that comprises a unique collection of datasets from the area of climate-vegetation dynamics. The results indicate that specialized time series classification methods are able to improve existing inference procedures. Substantial differences are observed among the methods that were tested

Advances in knowledge discovery and data mining Part II

19th Pacific-Asia Conference, PAKDD 2015, Ho Chi Minh City, Vietnam, May 19-22, 2015, Proceedings, Part II</p