skip to main content
research-article

Video streaming using a location-based bandwidth-lookup service for bitrate planning

Published: 06 August 2012 Publication History

Abstract

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, for example, using scalable video codecs or segmented adaptive HTTP streaming that switches between nonscalable 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 article 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.

Supplementary Material

a24-riiser-apndx.pdf (riiser.zip)
Supplemental movie, appendix, image and software files for, Video streaming using a location-based bandwidth-lookup service for bitrate planning.

References

[1]
Adobe. 2010. HTTP dynamic streaming on the Adobe Flash platform. http://www.adobe.com/products/httpdynamicstreaming/pdfs/httpdynamicstreaming_wp_ue.pdf.
[2]
Akamai. 2010. Akamai HD for iPhone encoding best practices. http://www.akamai.com/dl/whitepapers/Akamai_HDNetwork_Encoding_BP_iPhone_iPad.pdf.
[3]
Brandt, J. and Wolf, L. 2008. Adaptive video streaming for mobile clients. In Proceedings of the ACM International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV). 113--114.
[4]
Curcio, I. D. D., Vadakital, V. K. M., and Hannuksela, M. M. 2010. Geo-predictive real-time media delivery in mobile environment. In Proceedings of the ACM Multimedia International Conference. 3--8.
[5]
Diaz-Zayas, A., Merino, P., Panizo, L., and Recio, A. M. 2007. Evaluating video streaming over GPRS/UMTS networks: A practical case. In Proceedings of the IEEE Vehicular Technology Conference (VTC). 624--628.
[6]
Evensen, K., Kupka, T., Kaspar, D., Halvorsen, P., and Griwodz, C. 2010. Quality-adaptive scheduling for live streaming over multiple access networks. In Proceedings of the ACM International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV). 21--26.
[7]
Goyal, V. K. 2001. Multiple description coding: Compression meets the network. IEEE Signal Proce. Mag. 18, 5, 74--93.
[8]
Guo, M., Ammar, M. H., and Zegura, E. W. 2005. V3: A vehicle-to-vehicle live video streaming architecture. Pervas. Mobile Comput. 1, 4, 404--424.
[9]
Horsmanheimo, S., Jormakka, H., and Lähteenmäki, J. 2004. Location-aided planning in mobile network—trial results. Wirel. Personal Comm. 30, 207--216.
[10]
Hsu, C.-H. and Hefeeda, M. 2010. Achieving viewing time scalability in mobile video streaming using scalable video coding. In Proceedings of the ACM Multimedia International Conference. 111--122.
[11]
Huang, J., Krasic, C., Walpole, J., and Feng, W. 2003. Adaptive live video streaming by priority drop. In Proceedings of the IEEE International Conference on Advanced Video and Signal-Besed Surveillance. 342--347.
[12]
Johansen, D., Johansen, H., Aarflot, T., Hurley, J., Kvalnes, Â., Gurrin, C., Sav, S., Olstad, B., Aaberg, E., Endestad, T., Riiser, H., Griwodz, C., and Halvorsen, P. 2009. DAVVI: A prototype for the next generation multimedia entertainment platform. In Proceedings of the ACM Multimedia International Conference. 989--990.
[13]
Kaspar, D., Evensen, K., Engelstad, P. E., Hansen, A. F., Halvorsen, P., and Griwodz, C. 2010. Enhancing video-on-demand playout over multiple heterogeneous access networks. In Proceedings of the IEEE Consumer Communications and Networking Conference (CCNC). 47--51.
[14]
Krasic, C., Walpole, J., and Feng, W.-c. 2003. Quality-adaptive media streaming by priority drop. In Proceedings of the ACM International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV). 112--121.
[15]
Lee, K. C., Navarro, J. M., Chong, T. Y., Lee, U., and Gerla, M. 2010. Trace-based evaluation of rate adaptation schemes in vehicular environments. In Proceedings of the IEEE Vehicular Technology Conference (VTC).
[16]
Liva, G., Diaz, N. R., Scalise, S., Matuz, B., Niebla, C. P., Ryu, J.-G., Shin, M.-S., and Lee, H.-J. 2008. Gap filler architectures for seamless DVB-S2/RCS provision in the railway environment. In Proceedings of the IEEE Vehicular Technology Conference (VTC). 2996--3000.
[17]
Mähönen, P., Petrova, M., Riihijärvi, J., and Wellens, M. 2006. Cognitive wireless networks: your network just became a teenager. In Proceedings of IEEE INFOCOM.
[18]
Mai, C.-H., Huang, Y.-C., and Wei, H.-Y. 2010. Cross-layer adaptive H.264/AVC streaming over IEEE 802.11e experimental testbed. In Proceedings of the IEEE Vehicular Technology Conference (VTC).
[19]
Move Networks. 2008. Internet television: Challenges and opportunities. Tech. rep., Move Networks, Inc.
[20]
Netview Technology. 2010. http://www.netview.no/index.php?page=downloader.
[21]
Ni, P., Eichhorn, A., Griwodz, C., and Halvorsen, P. 2009. Fine-grained scalable streaming from coarse-grained videos. In Proceedings of the ACM International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV). 103--108.
[22]
Pantos, R., Batson, J., Biderman, D., May, B., and Tseng, A. 2010. HTTP live streaming. http://tools.ietf.org/html/draft-pantos-http-live-streaming-04.
[23]
Rejaie, R. and Ortega, A. 2003. PALS: peer-to-peer adaptive layered streaming. In Proceedings of the International Workshop on Network and Operating System Support for Digital Audio and Video (NOSSDAV). 153--161.
[24]
Riiser, H., Halvorsen, P., Griwodz, C., and Hestnes, B. 2008. Performance measurements and evaluation of video streaming in HSDPA networks with 16QAM modulation. In Preceedings of IEEE ICME. 489--492.
[25]
Riiser, H., Halvorsen, P., Griwodz, C., and Johansen, D. 2010. Low overhead container format for adaptive streaming. In Proceedings of the ACM Multimedia International Conference. 193--198.
[26]
Schierl, T., de la Fuente, Y. S., Globisch, R., Hellge, C., and Wiegand, T. 2010. Priority-based media delivery using SVC with RTP and HTTP streaming. Multimed. Tools Appl. (MTAP), 1--20.
[27]
Schwarz, H., Marpe, D., and Wiegand, T. 2007. Overview of the scalable video coding extension of the H.264/AVC standard. IEEE Trans. Circ. Syst. for Video Tech. 17, 9, 1103--1129.
[28]
Sun, J.-Z., Sauvola, J., and Riekki, J. 2005. Application of connectivity information for context interpretation and derivation. In Proceedings of ConTEL. 303--310.
[29]
Tamai, M., Sun, T., Yasumoto, K., Shibata, N., and Ito, M. 2004. Energy-aware QoS adaptation for streaming video based on MPEG-7. In Proceedings of IEEE ICME. 189--192.
[30]
Wac, K., van Halteren, A., and Konstantas, D. 2006a. Qos-predictions service: Infrastructural support for proactive qos- and context-aware mobile services (position paper). Lecture Notes in Computer Science, vol. 4278, Springer. 1924--1933.
[31]
Wac, K., van Halteren, A., and Konstantas, D. 2006b. QoS-predictions service: Infrastructural support for proactive QoS- and context-aware mobile services (position paper). In Proceedings of OTM Workshops. 1924--1933.
[32]
Zambelli, A. 2009. Smooth streaming technical overview. http://learn.iis.net/page.aspx/626/smooth-streaming-technical-overview/.
[33]
Zink, M., Künzel, O., Schmitt, J., and Steinmetz, R. 2003. Subjective impression of variations in layer encoded videos. In Proceedings of the IEEE International Workshop on Quality of Service. 137--154.

Cited By

View all
  • (2024)GreenABR+: Generalized Energy-Aware Adaptive Bitrate StreamingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364989820:9(1-24)Online publication date: 5-Mar-2024
  • (2024)Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile EnvironmentsIEEE/ACM Transactions on Networking10.1109/TNET.2023.327772932:1(50-64)Online publication date: Feb-2024
  • (2024)Throughput Prediction in Real-Time Communications: Spotlight on Traffic Extremes2024 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC61673.2024.10733668(1-7)Online publication date: 26-Jun-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 8, Issue 3
July 2012
143 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/2240136
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 August 2012
Accepted: 01 February 2011
Revised: 01 February 2011
Received: 01 November 2010
Published in TOMM Volume 8, Issue 3

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Adaptive streaming
  2. GPS
  3. bandwidth prediction
  4. bitrate planning
  5. fluctuating bandwidth
  6. mobile internet
  7. wireless

Qualifiers

  • Research-article
  • Research
  • Refereed

Funding Sources

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)19
  • Downloads (Last 6 weeks)1
Reflects downloads up to 19 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2024)GreenABR+: Generalized Energy-Aware Adaptive Bitrate StreamingACM Transactions on Multimedia Computing, Communications, and Applications10.1145/364989820:9(1-24)Online publication date: 5-Mar-2024
  • (2024)Joint Optimization of QoE and Fairness for Adaptive Video Streaming in Heterogeneous Mobile EnvironmentsIEEE/ACM Transactions on Networking10.1109/TNET.2023.327772932:1(50-64)Online publication date: Feb-2024
  • (2024)Throughput Prediction in Real-Time Communications: Spotlight on Traffic Extremes2024 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC61673.2024.10733668(1-7)Online publication date: 26-Jun-2024
  • (2024)BitFormer: Transformer-Based Neural Network for Bitrate Prediction in Real-Time Communications2024 IEEE 21st Consumer Communications & Networking Conference (CCNC)10.1109/CCNC51664.2024.10454679(65-70)Online publication date: 6-Jan-2024
  • (2024)DeX: Deep learning-based throughput prediction for real-time communications with emphasis on traffic eXtremesComputer Networks10.1016/j.comnet.2024.110507249(110507)Online publication date: Jul-2024
  • (2024)Caching in Location Based Services: Approaches, Challenges and Emerging TrendsWireless Personal Communications: An International Journal10.1007/s11277-024-11132-0135:3(1581-1615)Online publication date: 1-Apr-2024
  • (2023)Integrating Visual and Network Data with Deep Learning for Streaming Video Quality AssessmentSensors10.3390/s2308399823:8(3998)Online publication date: 14-Apr-2023
  • (2023)RTCoInfer: Real-Time Collaborative CNN Inference for Stream Analytics on Ubiquitous ImagesIEEE Journal on Selected Areas in Communications10.1109/JSAC.2023.324273041:4(1212-1226)Online publication date: 1-Apr-2023
  • (2022)Impact of User Playback Interactions on In-Network Estimation of Video Streaming PerformanceIEEE Transactions on Network and Service Management10.1109/TNSM.2022.318011419:3(3547-3561)Online publication date: Sep-2022
  • (2022)A Video Bitrate Adaptive Algorithm for Public Network Digital Trunking Terminals2022 3rd International Conference on Information Science, Parallel and Distributed Systems (ISPDS)10.1109/ISPDS56360.2022.9874181(355-359)Online publication date: 22-Jul-2022
  • Show More Cited By

View Options

Login options

Full Access

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media