Denny Stohr

An analysis of the YouNow live streaming platform

Video streaming platforms like Twitch.tv or YouNow have attracted the attention of both users and researchers in the last few years. Users increasingly adopt these platforms to share user-generated videos while researchers study their usage patterns to learn how to provide better and new services.

VAS: a video adaptation service to support mobile video

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.

EnvDASH: An Environment-Aware Dynamic Adaptive Streaming over HTTP System

The recent advances in adaptive video streaming technologies including Dynamic Adaptive Streaming over HTTP (DASH) are capable to adjust video streams to rapidly changing network conditions. Our system, EnvDASH, differs from those standard implementations as it extends DASH with mechanisms that allow sensing the environmental conditions of a device. EnvDASH leverages that users in mobile situations are often distracted from watching a video or that viewing conditions are severely degraded by adapting the video to reduce the generated network traffic. The system senses if the user is interested in watching a video, if the displaying device is held stable and if the ambient noise level allows listening to an audio track of a video. This is especially helpful as mobile devices usually use capped data volume contracts for the network access.

APP and PHY in Harmony: A framework enabling flexible physical layer processing to address application requirements

Mobile data traffic, particularly mobile video, grows at an unprecedented pace. Despite recent advances at the physical layer, todays wireless network infrastructure cannot keep up with this growth. This is partially due to the missing flexibility to adapt the physical layer continuously to best support both application level as well as network requirements. In this paper we show how to harness the flexibility of advanced physical layers in practice. We designed and implemented a research platform that provides a flexible application-centric physical layer for Android smartphones using software-defined radios (SDRs) as radio interfaces. Our solution allows applications to define flows and apply per-flow settings that are mapped into distinct physical layer settings. As a proof-of-concept and for testbed evaluation, we implemented our system together with a mobile video streaming application. The latter uses a Motion-JPEG based lightweight scalable video codec (SVC) to generate incremental data flows. We show that our system maximizes video quality at the receivers side, while keeping the energy consumption at the transmitter at a minimum. Our solution demonstrates that jointly optimizing network traffic and application quality is feasible in practice using a flexible physical layer processing approach.

Monitoring of User Generated Video Broadcasting Services

Mobile video broadcasting services offer users the opportunity to instantly share content from their mobile handhelds to a large audience over the Internet. However, existing data caps in cellular network contracts and limitations in their upload capabilities restrict the adoption of mobile video broadcasting services. 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 many cases, available network infrastructure is not capable to upload all video streams in parallel. To make decisions on how to appropriately transmit those video streams, a suitable monitoring of the video generation process is required. For this scenario, a measurement framework is proposed that allows Internet-scale mobile broadcasting services to deliver samples in an optimized way. Our framework architecture analyzes three zones for effectively monitoring user-generated video. Besides classical Quality of Service metrics on the network state, video quality indicators and additional auxiliary sensor information is gathered. Aim of this framework is an efficient coordination of devices and their uploads based on the currently observed system state.

QoE Analysis of DASH Cross-Layer Dependencies by Extensive Network Emulation

With the rising importance of video streaming in the Internet, dynamic adaptive streaming over HTTP (DASH) has been established as a key technology for video delivery. Yet, variable network conditions often result in a limited quality of experience (QoE)—with the interrelation of cross-layer network factors and DASH mechanisms widely unexplored. To understand the complex dependencies between DASH configurations and network conditions, we propose a systematic extensive large-scale emulation approach with state-of- the-art QoE metrics. Using this approach with a real DASH player in Mininet, we emulated more than 10, 000 combinations of static and dynamic network conditions and DASH configurations to derive their QoE. The obtained results show that no single DASH configuration provides the highest achievable QoE. Depending on the network conditions, specific combinations of the TCP congestion control, segment sizes and the DASH adaptation algorithm provide higher QoE—showing the possibility of performance improvements in practice. Furthermore, the ex- tensive emulations show a linear relation between delay, loss and QoE that is mostly independent of bandwidth.

Capture and Replay: Reproducible Network Experiments in Mininet

Network emulations are widely used in the networking community. The network emulator Mininet recently gained popularity, as it allows running real Linux applications on top of an emulated network. The specification of the network includes the topology as well as static bandwidth, latency, and packet drops probability parameters. Even though evaluations with static parameters provide useful insights, real world measurements show dynamically changing bandwidths, posing special challenges that need to be addressed in network research. In this demo, we capture bandwidth traces in the wild and reproducibly replay these traces in Mininet. Our \emph{capture and replay} infrastructure consists of a Mininet extension for replaying bandwidth traces, a measurement Android app, as well as a graphical repository for bandwidth traces. We exemplary demonstrate this toolchain for reproducible DASH and Multipath TCP experiments.

User-Generated Video Composition Based on Device Context Measurements

Instant sharing of user-generated video recordings has become a widely used service on platforms such as YouNow, Facebook.Live or uStream. Yet, providing such services with a high QoE for viewers is still challenging, given that mobile upload speed and capacities are limited, and the recording quality on mobile devices greatly depends on the users’ capabilities. One proposed solution to address these issues is video composition. It allows to switch between multiple recorded video streams, selecting the best source at any given time, for composing a live video with a better overall quality for the viewers. Previous approaches have required an in-depth visual analysis of the video streams, which usually limited the scalability of these systems. 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 a context-aware upload of video streams is evaluat...

Collaborations on YouTube: From Unsupervised Detection to the Impact on Video and Channel Popularity

YouTube is the most popular platform for streaming of user-generated videos. Nowadays, professional YouTubers are organized in so-called multichannel networks (MCNs). These networks offer services such as brand deals, equipment, and strategic advice in exchange for a share of the YouTubers’ revenues. A dominant strategy to gain more subscribers and, hence, revenue is collaborating with other YouTubers. Yet, collaborations on YouTube have not been studied in a detailed quantitative manner. To close this gap, first, we collect a YouTube dataset covering video statistics over 3 months for 7,942 channels. Second, we design a framework for collaboration detection given a previously unknown number of persons featured in YouTube videos. We denote this framework, for the detection and analysis of collaborations in YouTube videos using a Deep Neural Network (DNN)-based approach, as CATANA. Third, we analyze about 2.4 years of video content and use CATANA to answer research questions guiding YouTubers and MCNs for efficient collaboration strategies. Thereby, we focus on (1) collaboration frequency and partner selectivity, (2) the influence of MCNs on channel collaborations, (3) collaborating channel types, and (4) the impact of collaborations on video and channel popularity. Our results show that collaborations are in many cases significantly beneficial regarding viewers and newly attracted subscribers for both collaborating channels, often showing more than 100% popularity growth compared with noncollaboration videos.

Context Not Content: A Novel Approach to Real-Time User-Generated Video Composition

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.