High quality video streaming over cellular networks is expensive for users and often limited by data caps. Offloading the traffic over other cheap wireless networks, e.g. WLAN, would be beneficial however they are often not available for mobile streaming sessions. A potential solution is to load video to a mobile device before the user accesses it, which is called prefetching. Prefetching requires indicators to perform a prediction what a single user is going to watch in the future. This prediction determines which videos are loaded to the users' mobile device. We leverage recommender systems of the video sharing site YouTube to design and evaluate mobile prefetching algorithms. A user study has been performed over a month in which users' YouTube accesses were supported by our prefetching algorithms which allowed for significant reductions in data usage on mobile networks while having a low computational overhead.

Instant sharing of user-generated video recordings has become a widely used service on platforms such as YouNow. Yet, it still poses technical challenges, as mobile upload speed and capacities are limited. One proposed solution to address these issues is video composition. It allows switching between multiple video streams-selecting the best source for a given time-for composing a live video of a better overall quality for viewers. Previous approaches require visual analysis of the video streams, usually limiting the scalability of the system. In contrast, our work allows the stream selection to be realized solely on context information, based on video-and service-quality aspects from sensor and network measurements. The implemented monitoring service for context-aware upload of video streams is evaluated in varying network conditions, with diverse user behavior, including camera shaking and user mobility. We show that a higher efficiency for video upload as well as QoE for viewers can be achieved.

In comparison to professional productions, UGV often suffers from quality degradations such as camera shakes or harmful occlusions. Quick and reliable algorithms are needed to detect and assess the impact of these quality degradations. Recently, researchers leverage data from auxiliary sensors such as gyroscope readings or accelerometer values for quality assessment. In comparison to video-based approaches, auxiliary sensor-based ones achieve results more quickly but often lack precision.

Adaptive streaming systems gain rising relevance for streaming services. Therefore, the same video is offered in multiple quality versions to clients for adaptation during playback. However, optimizing adaptation in a Quality of Experience (QoE) centric way is difficult. Current systems maximize bit rate, ignoring that different types of adaptation (resolution, framerate, quantization) correlate differently and in a non-linear way with user's perception. User validated video quality metrics can provide precise quality information. However, measurements of state-of-the-art metrics show either high computational intensity or weak correlation with subjective tests. This makes large-scale offline quality assessment processing intensive while real-time constrained scenarios like live streaming and video conferencing are hardly supportable. Consequently, this work presents the Real-Time Video Quality Metric (RT-VQM), a real-time, Graphics Processing Unit (GPU) supported version of the widely used Video Quality Metric (VQM). RT-VQM introduces efficient filtering operations, hardware-supported scaling and high-performance feature pooling. The approach outperforms VQM by a factor of 30, thus enabling a real-time assessment of up to 9 parallel video stream representations up to High Definition (HD) 720 resolution at 30fps.

In most adaptive video streaming systems adaptation decisions rely solely on the available network resources. As the content of a video has a large influence on the perception of quality our belief is that this is not sufficient. Thus, we have proposed a support service for content-aware video adaptation on mobile devices: Video Adaptation Service (VAS). Based on the content of a streamed video, the adaptation process is improved by setting a target quality level for a session based on an objective video quality metric. In this work, we demonstrate VAS and its advantages of a reduced data traffic by only streaming the lowest video representation which is necessary to reach a desired quality. By leveraging the content properties of a video stream, the system is able to keep a stable video quality and at the same time reduce the network load.

Video streaming in wireless multihop networks is a challenge due to different capabilities of end-user devices and changing network conditions. This challenge is addressed at the application layer with adaptive video streaming schemes like dynamic adaptive streaming over HTTP (DASH), which is widely applied by content providers. DASH copes with diverse end-user device capabilities by storing several representations of the same video such that DASH can offer a video in multiple qualities to users. Nevertheless, adjustments in DASH are solely taking place at the application layer. Especially in wireless multihop networks, adaptions on the lower layers are of particular importance. Therefore, we propose a novel application-aware cross-layer framework which adapts network support structures at the network layer, performs resource allocation at the medium access layer, switches between communication types at the physical layer and takes into account the properties and requirements of DASH at the application layer. Furthermore, we present a unified graph model, which takes into account the application layer, the network layer, the medium access layer and the physical layer jointly. We formulate a binary linear problem which chooses the optimal video representation for each user and finds the best combination of mechanisms on the lower three layers to optimally distribute the video content through the wireless multihop network. We show that our application- aware cross-layer framework which utilizes transitions leads to gains between 15-83 % compared to conventional approaches that do not switch between different mechanisms.

This work discusses a distributed interactive video system that supports video annotation using simultaneous hyperlinking by multiple users. The users mark and annotate objects within the video with links to other media such as text, images, websites, or other videos. Annotations are visualized on the client user interface as an overlay close to the objects. Our system is intuitive to use; for example, it contains automatic object-tracking functionality that correctly positions the annotations, even when the form or location of an object changes. Thus, our first contribution discusses the adaptive object-tracking algorithm used for this repositioning. It shows improved precision and reliability in comparison to nonadaptive algorithms. A second key issue is to keep the system responsive when the number of concurrent annotators increases. Thus, we rely on the concept of eventual consistency between different network entities. While this weak form of consistency allows temporary inconsistencies, it ensures that a consistent state can be reached. Thus, the second contribution is the design and evaluation of our distributed interactive video system, which relies on the weak consistency paradigm.

Adaptive video streaming systems rely on the availability of different quality versions of a video. Such a system can dynamically adjust the quality of a video stream during its playback depending on the available network throughput. Even if the necessary throughput is available, mobile users can benefit from limiting the generated data traffic as most cellular network contracts have data caps. Usually, if the cap is reached, the throughput is throttled to a speed that does not allow video streaming. Existing systems react to varying network conditions but often neglect content-specific adaptation needs. Content inspection can help to save data traffic when a higher bitrate representation would not increase the perceived quality. In this work, we present the Video Adaptation Service (VAS), a support service for a content-aware video adaptation for mobile devices. Based on the video content, the adaptation process is improved for both the available network resources and the perception of the user. By leveraging the content properties of a video stream, the system is able to maintain a stable video quality and at the same time reduce the generated data traffic. The system is evaluated with different adaptation schemes and shows that content-specific adaptation can both increase the perceived quality as well as reduce the data traffic. Additionally, we demonstrate the practical feasibility of this approach by integrating the VAS into Dynamic Adaptive Streaming over the Hypertext Transfer Protocol.

Huge quantities of videos are shared via Online Social Networks (OSN) like Facebook and are watched on mobile devices. Internet connections via cellular networks (UMTS / LTE) require the scarce resources radio bandwidth and battery power. Prefetching of videos in areas of WLAN availability has the potential to reduce the power consumption in comparison to data transmission via cellular networks and prefetching can help to avoid users running into traffic caps of their network providers. Furthermore, startup delays can be reduced. Social networks offer contextual information such as likes and comments as well as social graph information which can potentially be used to predict which content will be consumed in the near future. In this paper, we elaborate possibilities to predict content consumption based on the number of likes, comments and the social graph distance. Our detailed analysis of the media access patterns of more than 700 users in Facebook shows that the media consumption does not solely depend on the number of likes or comments. Users tend to watch videos that are uploaded by close friends and family members. Furthermore, the time a video preview stays in the browser-viewport before being clicked (pre-click delay) can be exploited to decrease startup delays.

A recent trend in user-generated video is to broadcast live video from mobile phones. Mobile broadcasting platforms such as YouNow or Periscope understood this trend and attract multiple thousands of concurrent views per second. Amateur produced mobile live video often suffers from only a limited duration of the recordings. Usually, the live recordings do not cover entire events. To address this problem our approach tries to understand how to increase the recording duration by integrating features for influencing the user behavior including gamification.

Mobile network operators are expected to face significant traffic increase in the upcoming years. One alternative method is to intelligently move transmissions to times of network underutilization, either on 3G/4G or by offloading to WiFi. Video content, predicted by Cisco to constitute 69% of mobile traffic, offers the greatest potential for offloading. To this end, the demonstrated app strives to relieve the mobile network in a two ways. First, long-term prefetching of promising videos based on posts from the user's Online Social Network feed is performed. The knowledge about which video is likely being requested in the near future offers the opportunity to schedule the transmission according to its probability of being watched. Second, the approach is complemented with short-term prefetching, which is used whenever a content could not be downloaded by long-term prefetching. In this case, resources are optimized so as to maximize the communication efficiency while preserving the quality of service. The demonstrated app considers the smartphone's observed cellular network history to optimize the mobile throughput. A customized video player implements both the long-term and short-term prefetching. It reduces both the load on mobile networks, decreases playback pausing events and hereby achieves a high QoE. Thus, the player addresses both the operators' and the users' needs.

To compose high-quality movies directors need life-long learning and talent. User-generated video defines a new era of video production in which non-professionals record videos and share them on platforms such as YouTube. As hiring professional directors results in high costs, our work focuses on replacing those directors by crowdsourcing. The proposed system allows users to record and stream live videos to servers on which workers create a video mashup. A smartphone application for recording live video has been designed that supports the composition in the crowd by a multi-modal analysis of the recording quality. The contributions of this work are: The proposed system demonstrates that composing a large number of video views can be achieved in near real-time. Second, the system achieves comparable video quality for user-generated video in comparison to manual composition. Third, it offers insights on how to design real-time capable crowdsourcing systems. Fourth, by leveraging multi-modal features that can already be evaluated during recording the number of streams considered for presentation can be reduced.

Even though cellular networks offer a ubiquitous access to the Internet for mobile devices, their throughput is often insufficient for the rising demand for mobile video. Classical video streaming approaches can not cope with bandwidth fluctuations common in those networks. As a result adaptive approaches for video streaming have been proposed and are increasingly adopted on mobile devices. However, existing adaptive video systems often rely on available network resources alone. As video content properties have a large influence on the perception of occurring quality adaptations our belief is that this is not sufficient. In this work, we thus present a support service for a content-aware video adaptation on mobile devices. Based on the actual video content the adaptation process is improved for both the available network resources and the perception of the user. By leveraging the content properties of a video stream, the system is able to keep a stable video quality and at the same time reduce the network load.

Scalable video coding (SVC) can overcome the user heterogeneity issue, e.g., different screen resolutions or different connectivities, in video-streaming. In wireless multihop networks, the performance of SVC is not adequate, because SVC cannot adapt the lower layers. In order to adapt to changing environmental conditions, e.g., network topology, available resources or channel conditions, a cross-layer framework is required. We propose a new application-aware cross-layer framework which utilizes SVC, network structures and communication types at APP, NET, DLL and PHY layers together. Further, our application-aware cross-layer framework performs transitions at different layers to find the best combination of mechanisms. This is achieved by the following steps. First, we apply a graph-based approach to integrate all mechanisms on the different layers in a single graph. Secondly, video layers are modeled in the graph as virtual sources. Thirdly, we perform an optimal mapping from video layer data rates to physical layer rates. Fourthly, we formulate a multi-source sum rate optimization problem which chooses the best video layer distribution among users and the best combination of mechanisms at all layers. Finally, we demonstrate that our application-aware cross-layer framework outperforms current approaches.

Video sharing sites such as Youtube are prominent examples for the increasing demand of private camera owners to record, save and share their real life experiences as videos. Whereas professional video productions use advanced camera equipment in a controlled environment with skilled cameramen, User-Generated Video (UGV) differs significantly in regard of equipment, skill and thus in video quality. In this work we focus on a detailed analysis of the effects of camera shakes, harmful occlusions as well as a possible misalignment between the recording camera and the scene, on the resulting video quality. In contrast, most of the video quality discussion in the literature has focused on encoding artifacts and other compression problems. Our data was systematically gathered using large crowdsourcing experiments over three genres of video, and it was validated in a controlled lab setting. Our results show that even minimal camera shaking as well as occlusions lead to a significant reduction in the perceived video quality.

Mobile video broadcasting services offer the opportunity to instantly share video from mobile handhelds to a large audience over the Internet. Additionally, the quality of those video streams is often reduced by the lack of skills of recording users and the technical limitations of the video capturing devices. Our research focuses on large-scale events that attract dozens of users to record video in parallel. In such scenarios, our aim is to design a collaborative mobile video broadcasting service that composes video streams from multiple sources to create one video stream with enhanced perceived quality. Our realization of such a service combines high quality video streaming and at the same time reduces the network load.

The increasing capabilities of mobile handhelds to record high definition videos enable us to share moments with friends and the public. In large-scale events such as concerts, the manifold of potential views recorded by the audience allows to watch an event from different perspectives. In this work, we elaborate on the effects of different recording positions on quality and show that especially with retail smart phone camera sensors the quality differs as the position changes. This is a first systematic approach to describe why user-generated video sequences differ in terms of quality depending on changing recording positions. To achieve a sound understanding, we conduct a large-scale systematic subjective study using crowdsourcing, inspecting the effect of distance and orientation of a recorder in relation to the event taking place. Our results indicate an effect of the recording position on the perceived quality of a video.

Although the importance of video sharing and of social media is increasing from day to day, a full integration of videos into social media is not achieved yet. We have developed a system that maps the concept of hypervideo - allowing to annotate objects in a video - to social media. We define this combination as social video that simultaneously allows a large number of users to contribute to the content of a video. Users can annotate video objects by adding images, text, other videos, Web links, or even communication topics. An integrated chat system allows users to communicate with friends and to link these topics to distinct objects in the video. We analyze the technical functionality and the user acceptance of our social video system in detail. Due to the integration into the social network Facebook more than 12,000 users have already accessed our system.

We propose a novel approach for tracking video objects in interactive multimedia systems. Instead of designing a single tracking algorithm that works well with some templates (video objects to be tracked) but fails with others, our adaptive algorithm uses three distinct tracking techniques with different strengths and weaknesses. The tracking technique that is selected for a given template depends on its visual content and the content of the video. Our approach is robust and allows tracking requests of users to be computed despite an imprecise selection of a template. In addition, our approach is designed to comply with high precision, speed, and scalability to support many users simultaneously. As an exemplary testing environment for this tracking system, an interactive hypervideo system was chosen. The system was integrated into the social network Facebook and used by more than 12,000 users.