Abstract-Mobile Broadband (MBB) Networks are evolving at a fast pace, with technology enhancements that promise drastic improvements in capacity, connectivity, coverage, i.e., better performance in general. But how to measure the actual performance of a MBB solution? In this paper, we present our experience in running the simplest of the performance test: "speedtestlike" measurements to estimate the download speed offered by actual 3G/4G networks. Despite their simplicity, download speed measurements in MBB networks are much more complex than in wired networks, because of additional factors (e.g., mobility of users, physical impairments, diversity in technology, operator settings, mobile terminals diversity, etc.).We exploit the MONROE open platform, with hundreds of multihomed nodes scattered in 4 different countries, and explicitly designed with the goal of providing hardware and software solutions to run large scale experiments in MBB networks. We analyze datasets collected in 4 countries, over 11 operators, from about 50 nodes, for more than 2 months. After designing the experiment and instrumenting both the clients and the servers with active and passive monitoring tools, we dig into collected data, and provide insight to highlight the complexity of running even a simple speedtest. Results show interesting facts, like the occasional presence of NAT, and of Performance Enhancing Proxies (PEP), and pinpoint the impact of different network configurations that further complicate the picture. Our results will hopefully contribute to the debate about performance assessment in MBB networks, and to the definition of much needed benchmarks for performance comparisons of 3G, 4G and soon of 5G networks.

SummaryVideo streaming applications constitute a significant portion of the Internet traffic today, with mobile accounting for more than half of the online video views. The high share of video in the current Internet traffic mix has prompted many studies that examine video streaming through measurements. However, streaming performance depends on many different factors at different layers of the TCP/IP stack. For example, browser selection at the application layer or the choice of protocol in transport layer can have significant impact on the video performance. Furthermore, video performance heavily depends on the underlying network conditions (eg, network and link layers). For mobile networks, the conditions vary significantly, since each operator has a different deployment strategy and configuration. In this paper, we focus on YouTube and carry out a comprehensive study investigating the influence of different factors on streaming performance. Leveraging the Measuring Mobile Broadband Networks in Europe (MONROE) test bed that enables experimentation with 13 different network configurations in four countries, we collect more than 1800 measurement samples in operational mobile networks. With this campaign, our goal is to quantify the impact of parameters from different layers on YouTube's streaming quality of experience (QoE). More specifically, we analyze the role of the browser (eg, Firefox and Chrome), the impact of transport protocol (eg, TCP or QUIC), the influence of network bandwidth, and signal coverage on streaming QoE. Our analysis reveals that all these parameters need to be taken into account jointly for network management practices, in order to ensure a high end‐user experience.

As the demand for mobile connectivity continues to grow, there is a strong need to evaluate the performance of Mobile Broadband (MBB) networks. In the last years, mobile "speed", quantified most commonly by data rate, gained popularity as the widely accepted metric to describe their performance. However, there is a lack of consensus on how mobile speed should be measured. In this paper, we design and implement MONROE-Nettest to dissect mobile speed measurements, and investigate the effect of different factors on speed measurements in the complex mobile ecosystem. MONROE-Nettest is built as an Experiment as a Service (EaaS) on top of the MONROE platform, an open dedicated platform for experimentation in operational MBB networks. Using MONROE-Nettest, we conduct a large scale measurement campaign and quantify the effects of measurement duration, number of TCP flows, and server location on measured downlink data rate in 6 operational MBB networks in Europe. Our results indicate that differences in parameter configuration can significantly affect the measurement results. We provide the complete MONROE-Nettest toolset as open source and our measurements as open data.

No abstract

Developing systems for the automatic detection of events in video is a task which has gained attention in many areas including sports. More specifically, event detection for soccer videos has been studied widely in the literature. However, there are still a number of shortcomings in the state-of-the-art such as high latency, making it challenging to operate at the live edge. In this paper, we present an algorithm to detect events in soccer videos in real time, using 3D convolutional neural networks. We test our algorithm on three different datasets from SoccerNet, the Swedish Allsvenskan, and the Norwegian Eliteserien. Overall, the results show that we can detect events with high recall, low latency, and accurate time estimation. The trade-off is a slightly lower precision compared to the current state-of-the-art, which has higher latency and performs better when a less accurate time estimation can be accepted. In addition to the presented algorithm, we perform an extensive ablation study on how the different parts of the training pipeline affect the final results.

No abstract

No abstract

No abstract