This paper reflects on the development of a multi-sensory clubbing experience which was deployed during a two-day event within the context of the Amsterdam Dance Event in October 2016 in Amsterdam. We present how the entire experience was developed end-to-end and deployed at the event through the collaboration of several project partners from industries such as art and design, music, food, technology and research. Central to the system are smart textiles, namely wristbands equipped with Bluetooth LE sensors which were used to sense people attending the dance event. We describe the components of the system, the development process, the collaboration between the involved entities and the event itself. To conclude the paper, we highlight insights gained from conducting a real world research deployment across many collaborators and stakeholders with different backgrounds.@en

Dynamic Adaptive Streaming over HTTP (DASH) is the premier technology for Internet video streaming. DASH efficiently uses existing HTTP-based delivery infrastructures implementing adaptive streaming. However, DASH traffic is bursty in nature. This causes performance problems when DASH players share a network connection or in networks with heavy background traffic. The result is unstable and lower quality video. In this article, we present the design and implementation of a so-called DASH Assisting Network Element (DANE). Our system provides target bitrate signaling and dynamic traffic control. These two mechanisms realize proper bandwidth sharing among clients. Our system is privacy friendly and fully supports encrypted video streams. Trying to improve the streaming experience for users who share a network connection, our system increases the video bitrate and reduces the number of quality switches. We show this through evaluations in our Wi-Fi testbed.

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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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This article showcases our effort to explore the music club of the future. We present the development and results of an end-to-end system which enhances the club-going experience through the use of wearable technology. Each party guest wearing one of the wristbands actively contributes to the overall experience with their movement and location patterns. The system collects acceleration data from each of the attendees in real-time and feeds it into a pluggable network infrastructure, which processes the data, affecting the environment via data visualization or controlling of the light and sound system of a curated space within the club. Finally we describe the results of a two night, 450 person per night deployment.@en

The CWI-ADE2016 Dataset is a collection of more than 40 million Bluetooth Low Energy (BLE) packets and of 14 million accelerometer and temperature samples generated by wristbands that people wore in a nightclub. The data was gathered during Amsterdam Dance Event 2016 in an exclusive club experience curated around human senses, which leveraged technology as a bridge between the club and the guests. Each guest was handed a custom-made wristband with a BLE-enabled device that broadcast movement, temperature and other sensor readings. A network of Raspberry Pi receivers deployed for the occasion captured broadcast packets from wristbands and any other BLE device in the environment. This data provides a full picture of the performance of the real life deployment of a sensing infrastructure and gives insights to designing sensing platforms, understanding networks and crowds behaviour or studying opportunistic sensing. This paper describes an analysis of this dataset and some examples of usage.

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HTTP adaptive streaming (HAS) has become the dominant technology for streaming video over the Internet. It gained popularity because of its ability to adapt the video quality to the current network conditions and other appealing properties such as usage of off-the-shelf HTTP servers and easy firewall traversal. However, when multiple HAS players share a bottleneck link for streaming, the individual adaptation techniques in the players have difficulties to maintain a stable bitrate and fairly share the network resources. HAS-assisting network elements can solve these performance problems and allow execution of advanced policies for sharing the available bandwidth. Nonetheless, testing and evaluating new sharing policies is costly and time consuming. This motivated us to formulate a model that allows to differentiate between groups of users depending on the type of user or device, and that can describe the mean bitrate of the video streams and how often this bitrate is expected to change during playout. To show how our model can be used, we demonstrate two applications of our model. Furthermore, we validate the model based results against results obtained using our streaming testbed and proxy server based HAS-assistant. The results show that our model is highly accurate for both the mean bitrate and the number of changes in video bitrate. As such, our model is a useful tool for network administrators and internet service providers for evaluating the performance of sharing policies and for managing and provisioning video delivery networks.@en

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.@en

In this paper, we present a model for evaluating bandwidth sharing policies, that can be applied to networks that handle both video streaming traffic, as well as other traffic. Video streaming is a demanding network application. In crowded networks, resources need to be properly divided in order not to diminish the streaming experience. However, in network deployments with a large number of users, the streaming performance cannot be obtained straightforwardly from a sharing policy. Therefore, we propose a Markov model that is compatible with Dynamic Adaptive Streaming over HTTP (DASH), the major technology for video streaming over the Internet. If DASH is combined with in-network resource management, its performance can be significantly improved. Nevertheless, resource sharing policies need to be configured. This requires evaluation of many different configurations. Real deployments or network simulations demand many system resources and time. In contrast, our model can quickly evaluate many configurations, and for each configuration output the expected video bitrate and number of changes in video bitrate. These two parameters play an important role in the Quality of Experience of the viewer. In this paper, we demonstrate how our model can be used to analyze and optimize resource sharing policies. As such, our model is a useful tool for network administrators and allows them to better provision and configure their networks.@en