ABSTRACT
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.
- V. Adzic, H. Kalva, and B. Furht. Optimizing video encoding for adaptive streaming over HTTP. IEEE Transactions on Consumer Electronics, 2012.Google Scholar
- E. Akyol, A. M. Tekalp, and M. R. Civanlar. Content-Aware Scalability-Type Selection for Rate Adaptation of Scalable Video. EURASIP Journal on Advances in Signal Processing, 2007. Google ScholarDigital Library
- P. M. Eittenberger, M. Hamatschek, M. Großmann, and U. R. Krieger. Monitoring mobile video delivery to Android devices. In ACM Multimedia Systems Conference, 2013. Google ScholarDigital Library
- A. Fiandrotti, D. Gallucci, E. Masala, and J. De Martin. Content-adaptive traffic prioritization of spatio-temporal scalable video for robust communications over QoS-provisioned 802.11e networks. Signal Processing: Image Communication, 2010. Google ScholarDigital Library
- ITU. ITU-R Recommendation P.910, 2008.Google Scholar
- S. Lederer, C. Mueller, B. Rainer, M. Waltl, and C. Timmerer. An open source MPEG DASH evaluation suite. In IEEE Conference on Visual Communications and Image Processing, 2012.Google ScholarCross Ref
- Z. Li, A. C. Begen, J. Gahm, Y. Shan, B. Osler, and D. Oran. Streaming video over HTTP with consistent quality. In ACM Multimedia Systems Conference, 2014. Google ScholarDigital Library
- R. K. P. Mok, X. Luo, E. W. W. Chan, and R. K. C. Chang. QDASH: A QoE-aware DASH system. In ACM Multimedia Systems Conference, 2012. Google ScholarDigital Library
- A. K. Moorthy, L. K. Choi, and A. C. Bovik. Video Quality Assessment on Mobile Devices: Subjective, Behavioral and Objective Studies. IEEE Journal of Selected Topics in Signal Processing, 2012.Google Scholar
- M. Pinson and S. Wolf. A new standardized method for objectively measuring video quality. IEEE Transactions on Broadcasting, 50(3), 2004.Google ScholarCross Ref
- H. Schwarz, D. Marpe, and T. Wiegand. Overview of the Scalable Video Coding Extension of the H.264/AVC Standard. IEEE Transactions on Circuits and Systems for Video Technology, 2007. Google ScholarDigital Library
- T. Stockhammer. Dynamic Adaptive Streaming over HTTP: Standards and Design Principles. In ACM Multimedia Systems Conference, 2011. Google ScholarDigital Library
- X. Wang, M. Chen, T. T. Kwon, L. Yang, and V. C. M. Leung. AMES-Cloud: A Framework of Adaptive Mobile Video Streaming and Efficient Social Video Sharing in the Clouds. IEEE Transactions on Multimedia, 2013. Google ScholarDigital Library
- R. Zabih, J. Miller, and K. Mai. A feature-based algorithm for detecting and classifying production effects. Multimedia systems, 7(2), 1999. Google ScholarDigital Library
- M. Zink, O. Künzel, J. Schmitt, and R. Steinmetz. Subjective impression of variations in layer encoded videos. In International Conference on Quality of Service. Springer, 2003. Google ScholarDigital Library
- T. Zinner, O. Hohlfeld, O. Abboud, and T. Hossfeld. Impact of frame rate and resolution on objective QoE metrics. In International Workshop on Quality of Multimedia Experience, 2010.Google ScholarCross Ref
Index Terms
- VAS: a video adaptation service to support mobile video
Recommendations
A Content-Aware Video Adaptation Service to Support Mobile Video
Special Section on Multimedia Big Data: Networking and Special Section on Best Papers From ACM MMSYS/NOSSDAV 2015Adaptive video streaming systems rely on the availability of different quality versions of a video. Such a system can dynamically adjust the quality of a video stream during its playback depending on the available network throughput. Even if the ...
The content-aware video adaptation service for mobile devices
MMSys '16: Proceedings of the 7th International Conference on Multimedia SystemsIn most adaptive video streaming systems adaptation decisions rely solely on the available network resources. As the content of a video has a large influence on the perception of quality our belief is that this is not sufficient. Thus, we have proposed ...
Can You See What I See? Quality-of-Experience Measurements of Mobile Live Video Broadcasting
Broadcasting live video directly from mobile devices is rapidly gaining popularity with applications like Periscope and Facebook Live. The quality of experience (QoE) provided by these services comprises many factors, such as quality of transmitted ...
Comments