15 research outputs found
Improving Mobile Video Streaming with Mobility Prediction and Prefetching in Integrated Cellular-WiFi Networks
We present and evaluate a procedure that utilizes mobility and throughput
prediction to prefetch video streaming data in integrated cellular and WiFi
networks. The effective integration of such heterogeneous wireless technologies
will be significant for supporting high performance and energy efficient video
streaming in ubiquitous networking environments. Our evaluation is based on
trace-driven simulation considering empirical measurements and shows how
various system parameters influence the performance, in terms of the number of
paused video frames and the energy consumption; these parameters include the
number of video streams, the mobile, WiFi, and ADSL backhaul throughput, and
the number of WiFi hotspots. Also, we assess the procedure's robustness to time
and throughput variability. Finally, we present our initial prototype that
implements the proposed approach.Comment: 7 pages, 15 figure
Mobility: a double-edged sword for HSPA networks
This paper presents an empirical study on the performance of mobile High Speed Packet Access (HSPA, a 3.5G cellular standard) networks in Hong Kong via extensive field tests. Our study, from the viewpoint of end users, covers virtually all possible mobile scenarios in urban areas, including subways, trains, off-shore ferries and city buses. We have confirmed that mobility has largely negative impacts on the performance of HSPA networks, as fast-changing wireless environment causes serious service deterioration or even interruption. Meanwhile our field experiment results have shown unexpected new findings and thereby exposed new features of the mobile HSPA networks, which contradict commonly held views. We surprisingly find out that mobility can improve fairness of bandwidth sharing among users and traffic flows. Also the triggering and final results of handoffs in mobile HSPA networks are unpredictable and often inappropriate, thus calling for fast reacting fallover mechanisms. We have conducted in-depth research to furnish detailed analysis and explanations to what we have observed. We conclude that mobility is a double-edged sword for HSPA networks. To the best of our knowledge, this is the first public report on a large scale empirical study on the performance of commercial mobile HSPA networks
Anticipatory Buffer Control and Quality Selection for Wireless Video Streaming
Video streaming is in high demand by mobile users, as recent studies
indicate. In cellular networks, however, the unreliable wireless channel leads
to two major problems. Poor channel states degrade video quality and interrupt
the playback when a user cannot sufficiently fill its local playout buffer:
buffer underruns occur. In contrast to that, good channel conditions cause
common greedy buffering schemes to pile up very long buffers. Such
over-buffering wastes expensive wireless channel capacity.
To keep buffering in balance, we employ a novel approach. Assuming that we
can predict data rates, we plan the quality and download time of the video
segments ahead. This anticipatory scheduling avoids buffer underruns by
downloading a large number of segments before a channel outage occurs, without
wasting wireless capacity by excessive buffering. We formalize this approach as
an optimization problem and derive practical heuristics for segmented video
streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed
measurements show that our solution essentially eliminates playback
interruptions without significantly decreasing video quality
Behind the NAT â A Measurement Based Evaluation of Cellular Service Quality
AbstractâMobile applications such as VoIP, (live) gaming, or video streaming have diverse QoS requirements ranging from low delay to high throughput. The optimization of the network quality experienced by end-users requires detailed knowledge of the expected network performance. Also, the achieved service quality is affected by a number of factors, including network operator and available technologies. However, most studies focusing on measuring the cellular network do not consider the performance implications of network configuration and management. To this end, this paper reports about an extensive data set of cellular network measurements, focused on analyzing root causes of mobile network performance variability. Measurements conducted over four weeks in a 4G cellular network in Germany show that management and configuration decisions have a substantial impact on the performance. Specifically, it is observed that the association of mobile devices to a Point of Presence (PoP) within the operatorâs network can influence the end-to-end RTT by a large extent. Given the collected data a model predicting the PoP assignment and its resulting RTT leveraging Markov Chain and machine learning approaches is developed. RTT increases of 58% to 73% compared to the optimum performance are observed in more than 57% of the measurements
A Context-Aware Model to Improve Usability of Information Display on Smartphone Apps for Emerging Users
Smartphones have become a reliable technology for accessing information and services in rural communities. Mobile applications, such as social media and news apps running on smartphones, are no longer exclusively utilised by users in developed communities. Mobile applications are accessed in highly contextualised environments. This paper discusses a context-aware model that was implemented to improve the usability of information presented on smartphone applications for emerging users. User evaluation was conducted within a remote area in South Africa with a sample of users, most of whom did not have prior experience in using computer applications. The results of the evaluation present empirical evidence that the model can improve the usefulness of mobile applications and their adoption in rural areas by emerging users who primarily rely on smartphones for accessing a variety of information sources and services. The findings can be utilised as a blueprint for implementing sustainable mobile interventions for emerging users
Proactive Received Power Prediction Using Machine Learning and Depth Images for mmWave Networks
This study demonstrates the feasibility of the proactive received power
prediction by leveraging spatiotemporal visual sensing information toward the
reliable millimeter-wave (mmWave) networks. Since the received power on a
mmWave link can attenuate aperiodically due to a human blockage, the long-term
series of the future received power cannot be predicted by analyzing the
received signals before the blockage occurs. We propose a novel mechanism that
predicts a time series of the received power from the next moment to even
several hundred milliseconds ahead. The key idea is to leverage the camera
imagery and machine learning (ML). The time-sequential images can involve the
spatial geometry and the mobility of obstacles representing the mmWave signal
propagation. ML is used to build the prediction model from the dataset of
sequential images labeled with the received power in several hundred
milliseconds ahead of when each image is obtained. The simulation and
experimental evaluations using IEEE 802.11ad devices and a depth camera show
that the proposed mechanism employing convolutional LSTM predicted a time
series of the received power in up to 500 ms ahead at an inference time of less
than 3 ms with a root-mean-square error of 3.5 dB