Mobile networks, especially LTE networks, are used more and more for high-bandwidth services like multimedia or video streams. The quality of the data connection plays a major role in the perceived quality of a service. Videos may be presented in a low quality or experience a lot of stalling events, when the connection is too slow to buffer the next frames for playback. So far, no publicly available data set exists that has a larger number of LTE network traces and can be used for deeper analysis. In this data set, we provide 546 traces of 5 minutes each with a sample rate of 100 ms. Thereof 377 traces are pure LTE data. We furthermore provide an Android app to gather further traces as well as R scripts to clean, sort, and analyze the data.

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Multiscreen TV viewing refers to a spectrum of media productions that can be watched using TV and companion screens such as smartphones and tablets. In the last several years, companies are creating companion applications to enrich the TV viewing experience, but viewers are demotivated to consume them because they have to download dozens of second screen applications. This paper proposes to integrate the creation of companion screen content in a single object-based preproduction tool. It identifies, from the perspective of TV production professionals, the best paradigm and the needed features to support content authoring for multiscreen viewing experiences.

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Frequent variations in throughput make mobile networks a challenging environment for video streaming. Current video players deal with those variations by matching video quality to network throughput. However, this adaptation strategy results in frequent changes of video resolution and bitrate, which negatively impacts the users' streaming experience. Alternatively, keeping the video quality constant would improve the experience, but puts additional demand on the network. Downloading high quality content when channel quality is low requires additional resources, because data transfer efficiency is linked to channel quality. In this paper, we present a predictive Channel Quality based Buffering Strategy (CQBS) that lets the video buffer grow when channel quality is good, and relies on this buffer when channel quality decreases. Our strategy is the outcome of a Markov Decision Process. The underlying Markov chain is conditioned on 377 real-world LTE channel quality traces that we have collected using an Android mobile application. With our strategy, mobile network providers can deliver constant quality video streams, using less network resources.

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Videos are an important part of social platforms. With growing data speeds and high resolution cameras on mobile devices and smartphones, mobile instant and live video clip sharing become increasingly popular. However, video uploads are resource consuming which leads to long upload times, especially in environments with poor data connections. In current mobile applications, the user has little to no influence on optimizing the upload of her/his video according to the current (network) context. In this work, we propose a mobile application that shows an accurate upload time estimation and a current network speed indication. The user can select a video quality for uploading and by that possibly reach faster uploads in low bandwidth connection areas. In a user study with 21 users, we show that users perceive the upload speed as higher with given upload estimation and network speed indication when they have less bandwidth available. With this information, participants perceive the application as more reliable and have an increased feeling of control over the upload process. All users liked the proposed video quality customization feature. Compared to a graphical representation of the network speed, the upload time was the more helpful information to customize the upload.

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Multi-screen applications have been a research topic for the last 10 years. Recent technical advances make authoring and broadcasting of interactive multi-platform experiences possible. However, most of the efforts have been dedicated to the delivery and transmission technology (e.g., HbbTV2.0), but not to the production process. The hypothesis of this paper is that studio and outside broadcast production requires radical changes in the production workflow, in order to allow for an efficient management of interactive multi-platform experiences. This paper explores such changes, investigating workflows and roles, and identifying key requirements for supporting these. The final objective is to create a new set of tools, which are extending current processes, that allow broadcasters to curate new types of experiences. We conducted a set of interviews with broadcast producers and directors that allowed us to identify two major (sub-)workflows, one for pre-recorded and one for live experiences. We could then assign roles to the different stages of the workflows and derive a number of requirements for the next generation of production tools.@en

Dynamic Adaptive Streaming over HTTP (DASH) is a technology for delivering video content over the Internet. It provides an effective mechanism, which has been adopted by major content providers. Nevertheless, available DASH player implementations have a number of drawbacks such as performance problems on shared network connections, which lead to video freezes and frequent video quality changes. In this paper, we propose a method to reduce the performance problems that exist in networks with a large number of DASH players. These networks can be found in hotels, apartment complexes, and airports. In experiments with up to 600 simultaneously active players, we are able to reduce the number of DASH players with freezes by 95% (from 345 to 15) compared to throughput-based adaptation and by 75% (from 62 to 15) compared to BOLA using our DASH Assisting Network Element DANE). In addition, we reduced the number of quality switches by 94% compared to throughput-based adaptation, and by 85% compared to BOLA.@en