7,985 research outputs found

    Maximizing Resource Utilization In Video Streaming Systems

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    Video streaming has recently grown dramatically in popularity over the Internet, Cable TV, and wire-less networks. Because of the resource demanding nature of video streaming applications, maximizing resource utilization in any video streaming system is a key factor to increase the scalability and decrease the cost of the system. Resources to utilize include server bandwidth, network bandwidth, battery life in battery operated devices, and processing time in limited processing power devices. In this work, we propose new techniques to maximize the utilization of video-on-demand (VOD) server resources. In addition to that, we propose new framework to maximize the utilization of the network bandwidth in wireless video streaming systems. Providing video streaming users in a VOD system with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait thereby increasing server utilization by increasing server throughput. In this work, we analyze waiting-time predictability in scalable video streaming. We also propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits. The achieved resource sharing by stream merging depends greatly on how the waiting requests are scheduled for service. Motivated by the development of cost-based scheduling, we investigate its effectiveness in great detail and discuss opportunities for further tunings and enhancements. Additionally, we analyze the effectiveness of incorporating video prediction results into the scheduling decisions. We also study the interaction between scheduling policies and the stream merging techniques and explore new ways for enhancements. The interest in video surveillance systems has grown dramatically during the last decade. Auto-mated video surveillance (AVS) serves as an efficient approach for the realtime detection of threats and for monitoring their progress. Wireless networks in AVS systems have limited available bandwidth that have to be estimated accurately and distributed efficiently. In this research, we develop two cross-layer optimization frameworks that maximize the bandwidth optimization of 802.11 wireless network. We develop a distortion-based cross-layer optimization framework that manages bandwidth in the wire-less network in such a way that minimizes the overall distortion. We also develop an accuracy-based cross-layer optimization framework in which the overall detection accuracy of the computer vision algorithm(s) running in the system is maximized. Both proposed frameworks manage the application rates and transmission opportunities of various video sources based on the dynamic network conditions to achieve their goals. Each framework utilizes a novel online approach for estimating the effective airtime of the network. Moreover, we propose a bandwidth pruning mechanism that can be used with the accuracy-based framework to achieve any desired tradeoff between detection accuracy and power consumption. We demonstrate the effectiveness of the proposed frameworks, including the effective air-time estimation algorithms and the bandwidth pruning mechanism, through extensive experiments using OPNET

    An altruistic cross-layer recovering mechanism for ad hoc wireless networks

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    Video streaming services have restrictive delay and bandwidth constraints. Ad hoc networks represent a hostile environment for this kind of real-time data transmission. Emerging mesh networks, where a backbone provides more topological stability, do not even assure a high quality of experience. In such scenario, mobility of terminal nodes causes link breakages until a new route is calculated. In the meanwhile, lost packets cause annoying video interruptions to the receiver. This paper proposes a new mechanism of recovering lost packets by means of caching overheard packets in neighbor nodes and retransmit them to destination. Moreover, an optimization is shown, which involves a video-aware cache in order to recover full frames and prioritize more significant frames. Results show the improvement in reception, increasing the throughput as well as video quality, whereas larger video interruptions are considerably reduced. Copyright © 2014 John Wiley & Sons, Ltd.Arce Vila, P.; Guerri Cebollada, JC. (2015). An altruistic cross-layer recovering mechanism for ad hoc wireless networks. Wireless Communications and Mobile Computing. 15(13):1744-1758. doi:10.1002/wcm.2459S174417581513Li J Blake C De Couto DSJ Lee HI Morris R Capacity of ad hoc wireless networks Proceedings of the 7th Annual International Conference on Mobile Computing and Networks (MobiCom) 2001 61 69Akyildiz, I. F., & Xudong Wang. (2005). A survey on wireless mesh networks. IEEE Communications Magazine, 43(9), S23-S30. doi:10.1109/mcom.2005.1509968Hsu, C.-J., Liu, H.-I., & Seah, W. K. G. (2011). Opportunistic routing – A review and the challenges ahead. Computer Networks, 55(15), 3592-3603. doi:10.1016/j.comnet.2011.06.021Huang, X., Zhai, H., & Fang, Y. (2008). Robust cooperative routing protocol in mobile wireless sensor networks. IEEE Transactions on Wireless Communications, 7(12), 5278-5285. doi:10.1109/t-wc.2008.060680Wieselthier, J. E., Nguyen, G. D., & Ephremides, A. (2001). Mobile Networks and Applications, 6(3), 251-263. doi:10.1023/a:1011478717164Clausen T Jacquet P Optimized Link State Routing Protocol (OLSR), IETF RFC 3626 2003 http://www.rfc-editor.org/rfc/rfc3626.txtMarina, M. K., & Das, S. R. (2006). Ad hoc on-demand multipath distance vector routing. Wireless Communications and Mobile Computing, 6(7), 969-988. doi:10.1002/wcm.432Zhou X Lu Y Ma HG Routing improvement using multiple disjoint paths for ad hoc networks International Conference on Wireless and Optical Communications Networks (IFIP) 2006 1 5Fujisawa H Minami H Yamamoto M Izumi Y Fujita Y Route selection using retransmission packets for video streaming on ad hoc networks IEEE Conference on Radio and Wireless Symposium (RWS) 2006 607 610Badis H Agha KA QOLSR multi-path routing for mobile ad hoc networks based on multiple metrics: bandwidth and delay IEEE 59th Vehicular Technology Conference (VTC) 2004 2181 2184Wu Z Wu J Cross-layer routing optimization for video transmission over wireless ad hoc networks 6th International Conference on Wireless Communications Networks and Mobile Computing (WiCOM) 2010 1 6Schier, M., & Welzl, M. (2012). Optimizing Selective ARQ for H.264 Live Streaming: A Novel Method for Predicting Loss-Impact in Real Time. IEEE Transactions on Multimedia, 14(2), 415-430. doi:10.1109/tmm.2011.2178235Nikoupour M Nikoupour A Dehghan M A cross-layer framework for video streaming over wireless ad-hoc networks 3rd International Conference on Digital Information Management (ICDIM) 2008 340 345Yamamoto R Miyoshi T Distributed retransmission method using neighbor terminals for ad hoc networks Proceedings of the 14th Asia-Pacific Conference on Communications (APCC) 2008 1 5Gravalos I Kokkinos P Varvarigos EA Multi-criteria cooperative energy-aware routing in wireless ad-hoc networks Proceedings of the 9th International Wireless Communications and Mobile Computing Conference (IWCMC) 2013 387 393Abid, R. M., Benbrahim, T., & Biaz, S. (2010). IEEE 802.11s Wireless Mesh Networks for Last-Mile Internet Access: An Open-Source Real-World Indoor Testbed Implementation. Wireless Sensor Network, 02(10), 725-738. doi:10.4236/wsn.2010.210088Yen, Y.-S., Chang, R.-S., & Wu, C.-Y. (2011). A seamless handoff scheme for IEEE 802.11 wireless networks. Wireless Communications and Mobile Computing, 13(2), 157-169. doi:10.1002/wcm.1102Liangzhong Yin, & Guohong Cao. (2006). Supporting cooperative caching in ad hoc networks. IEEE Transactions on Mobile Computing, 5(1), 77-89. doi:10.1109/tmc.2006.15Biswas S Morris R ExOR: opportunistic multi-hop routing for wireless networks Proceedings of ACM SIGCOMM 2005 133 144Chachulski S Jennings M Katti S Katabi D Trading structure for randomness in wireless opportunistic routing Proceedings of ACM SIGCOMM 2007 169 180Kohler E Handley M Floyd S Datagram Congestion Control Protocol (DCCP), IETF RFC 4340 2006 http://www.rfc-editor.org/rfc/rfc4340.txtSchierl, T., Ganger, K., Hellge, C., Wiegand, T., & Stockhammer, T. (2006). SVC-based multisource streaming for robust video transmission in mobile ad hoc networks. IEEE Wireless Communications, 13(5), 96-103. doi:10.1109/wc-m.2006.250365Iera, A., Molinaro, A., Paratore, S. Y., Ruggeri, G., & Zurzolo, A. (2011). Making a mesh router/gateway from a smartphone: Is that a practical solution? Ad Hoc Networks, 9(8), 1414-1429. doi:10.1016/j.adhoc.2011.03.00

    Downlink Video Streaming for Users Non-Equidistant from Base Station

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    We consider multiuser video transmission for users that are non-equidistantly positioned from base station. We propose a greedy algorithm for video streaming in a wireless system with capacity achieving channel coding, that implements the cross-layer principle by partially separating the physical and the application layer. In such a system the parameters at the physical layer are dependent on the packet length and the conditions in the wireless channel and the parameters at the application layer are dependent on the reduction of the expected distortion assuming no packet errors in the system. We also address the fairness in the multiuser video system with non-equidistantly positioned users. Our fairness algorithm is based on modified opportunistic round robin scheduling. We evaluate the performance of the proposed algorithms by simulating the transmission of H.264/AVC video signals in a TDMA wireless system
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