In this dataset paper, we present and make available realworld measurements of the throughput that was achieved at the application layer when adaptive HTTP streaming was performed over 3G networks using mobile devices. For the streaming sessions, we used popular commute routes in and around Oslo (Norway) traveling with different types of public transportation (metro, tram, train, bus and ferry). We also have a few logs using a car. Each log provides a timestamp, GPS coordinates and the measured number of bytes downloaded for approximately every second of the route. The dataset can be used in several ways, but the most obvious application is to emulate the same network bandwidth behavior (on specific geographical positions) for repeated experiments.

A lot of people around the world commute using public transportation and would like to spend this time viewing streamed video content such as news or sports updates. However, mobile wireless networks typically suffer from severe bandwidth fluctuations, and the networks are often completely unresponsive for several seconds, sometimes minutes. Today, there are several ways of adapting the video bitrate and thus the video quality to such fluctuations, e.g., using scalable video codecs or segmented adaptive HTTP streaming that switches between non-scalable video streams encoded in different bitrates. Still, for a better long-term video playout experience that avoids disruptions and frequent quality changes while using existing video adaptation technology, it is desirable to perform bandwidth prediction and planned quality adaptation. This paper describes a video streaming system for receivers equipped with a GPS. A receiver's download rate is constantly monitored, and periodically reported back to a central database along with associated GPS positional data. Thus, based on the current location, a streaming device can use a GPS-based bandwidth-lookup service in order to better predict the near-future bandwidth availability and create a schedule for the video playout that takes likely future availability into account. To create a prototype and perform initial tests, we conducted several field trials while commuting using public transportation. We show how our database has been used to predict bandwidth fluctuations and network outages, and how this information helps maintain uninterrupted playback with less compromise on video quality than possible without prediction.

There are many available commercial streaming solutions that perform quality adaption. An important issue with respect to users' perceived quality is how the system schedules the quality levels to match the available network resources. In this study, we compare several adaptive media players on the market to see how they perform in challenging streaming scenarios on a mobile 3G network. Bandwidth data collected in real-world field trials is used in all tests. We investigate how the media players respond to fluctuating bandwidth and outages, and how this affects the quality levels used, the bandwidth utilization, and the number and duration of buffer underruns. We found significant differences in performance and optimization goals between the different players' schedulers. We conclude that the quality scheduler is an important factor in providing a satisfying quality of experience when using an adaptive media player.

A well known challenge with mobile video streaming is fluctuating bandwidth. As the client devices move in and out of network coverage areas, the users may experience varying signal strengths, competition for the available resources and periods of network outage. These conditions have a significant effect on video quality.In this paper, we present a video streaming solution for roaming clients that is able to compensate for the effects of oscillating bandwidth through bandwidth prediction and video quality scheduling. We combine our existing adaptive segmented HTTP streaming system with 1) an application layer framework for creating transparent multi-link applications, and 2) a location-based QoS information system containing GPS coordinates and accompanying bandwidth measurements, populated through crowd-sourcing. Additionally, we use real-time traffic information to improve the prediction by, for example, estimating the length of a commute route. To evaluate our prototype, we performed realworld experiments using a popular tram route in Oslo, Norway. The client connected to multiple networks, and the results show that our solution increases the perceived video quality significantly. Also, we used simulations to evaluate the potential of aggregating bandwidth along the route.

One of the main challenges when streaming video to mobile devices is to handle fluctuating bandwidth and frequent network outages as the device is brought in and out of areas with network coverage. In this paper, we propose bitrate and video quality planning algorithms for mobile streaming scenarios using a GPS-based bandwidth-lookup service in order to reduce frequently changing video quality and the number of playout interruptions. Our real world experiments using an adaptive segmented HTTP video streaming system while travelling on popular commute routes indicate that the users' quality of experience is greatly increased.Index Terms-adaptive streaming, GPS-based bandwidth prediction, bitrate and quality planning