8,780 research outputs found

    Novel Approach for Evaluating Video Transmission using Combined Scalable Video Coding over Wireless Broadband Network

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    One of the main problems in video transmission is the bandwidth fluctuation in wireless channel. Therefore, there is an urgent need to find an efficient bandwidth utilization and method. This research utilizes the Combined Scalable Video Coding (CSVC) which comes from Joint Scalable Video Model (JSVM). In the combined scalable video coding, we implement Coarse Grain Scalability (CGS) and Medium Grain Scalability (MGS). We propose a new scheme in which it can be implemented on Network Simulator II (NS-2) over wireless broadband network. The advantages of this new scheme over the other schemes are more realistic and based on open source program. The result shows that CSVC implementation on MGS mode outperforms CGS mode

    Scalable video/image transmission using rate compatible PUM turbo codes

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    The robust delivery of video over emerging wireless networks poses many challenges due to the heterogeneity of access networks, the variations in streaming devices, and the expected variations in network conditions caused by interference and coexistence. The proposed approach exploits the joint optimization of a wavelet-based scalable video/image coding framework and a forward error correction method based on PUM turbo codes. The scheme minimizes the reconstructed image/video distortion at the decoder subject to a constraint on the overall transmission bitrate budget. The minimization is achieved by exploiting the rate optimization technique and the statistics of the transmission channel

    Enhancing the quality of video streaming over unreliable wireless networks

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    University of Technology Sydney. Faculty of Engineering and Information Technology.Real-time video transmission over unreliable wireless networks remains a serious challenge due to bandwidth limitation and sensitive nature of video bitstreams generated by today’s complex video encoders, e.g., High Efficiency Video Coding (HEVC/H.265). These compressed video bitsreams face packet-drop problem when transmitted over unreliable wireless networks. The effect of packet-drop on the received video quality can be minimised in two ways 1) increasing Quality of Service (QoS) by adopting efficient routing schemes between source and destination, and 2) maintaining video quality at receiver’s side by applying smart and real-time-based Error Concealment (EC) techniques. The QoS refers to the capability of a transmission network to provide better service to selected network traffic. It is a generic term and can be applied to any data transmission network. The term video quality refers to perceived video degradation and is compared to the original video. In this dissertation, we explore the above mentioned two ways and propose a comprehensive solution for real-time video transmission over unreliable networks with the contributions as follows. 1. An efficient, lightweight and real-time EC algorithm is proposed to conceal the missing/lost video frames in H.265 encoded HD videos. The EC algorithm is based on threshold-based distributed Motion Estimation (ME) scheme and utilises only two video frames to estimate the missing one, thus eliminating the need for a large buffer and processing of a bundle of video frames to estimate the missing one. 2. Scalable video coding produces multiple interrelated bitstreams of a single video with different bitrates. For scalable bitstreams, we propose a lightweight and real-time EC algorithm to cover up the effects of missing/lost video frames. Due to complicated nature of scalable video bitstreams, our proposed EC algorithm utilises three previously processed video frames along with their master video frames to perform threshold-based distributed ME to estimate the missing video frames in enhancement layer. 3. We propose a feed-back-based on-demand multipath routing scheme over a multi-hop Wireless Multimedia Sensor Network (WMSN) to ensure the QoS. The feedback helps in deciding the optimum path between sources and destinations and reduces the Packets Loss Ratio (PLR) during the transmissions. On-demand connection assists in saving the available network resources while multipath routing aids in maintaining the connection between sources and destinations. The proposed research makes notable contributions to designing a QoS-supported HD video streaming paradigm to deliver HD videos over unreliable networks and to maintain the received video quality on resource-constrained mobile terminals

    SVCEval-RA: an evaluation framework for adaptive scalable video streaming

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    [EN] Multimedia content adaption strategies are becoming increasingly important for effective video streaming over the actual heterogeneous networks. Thus, evaluation frameworks for adaptive video play an important role in the designing and deploying process of adaptive multimedia streaming systems. This paper describes a novel simulation framework for rate-adaptive video transmission using the Scalable Video Coding standard (H.264/SVC). Our approach uses feedback information about the available bandwidth to allow the video source to select the most suitable combination of SVC layers for the transmission of a video sequence. The proposed solution has been integrated into the network simulator NS-2 in order to support realistic network simulations. To demonstrate the usefulness of the proposed solution we perform a simulation study where a video sequence was transmitted over a three network scenarios. The experimental results show that the Adaptive SVC scheme implemented in our framework provides an efficient alternative that helps to avoid an increase in the network congestion in resource-constrained networks. Improvements in video quality, in terms of PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index) are also obtained.Castellanos Hernández, WE.; Guerri Cebollada, JC.; Arce Vila, P. (2017). SVCEval-RA: an evaluation framework for adaptive scalable video streaming. Multimedia Tools and Applications. 76(1):437-461. doi:10.1007/s11042-015-3046-yS437461761Akhshabi S, Begen AC, Dovrolis C (2011) An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP. In: Proceedings of the second annual ACM conference on Multimedia systems. ACM, pp 157–168Alabdulkarim MN, Rikli N-E (2012) QoS Provisioning for H.264/SVC Streams over Ad-Hoc ZigBee Networks Using Cross-Layer Design. In: 8th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM). pp 1–8Birkos K, Tselios C, Dagiuklas T, Kotsopoulos S (2013) Peer selection and scheduling of H. 264 SVC video over wireless networks. In: Wireless Communications and Networking Conference (WCNC), 2013 IEEE. pp 1633–1638Castellanos W (2014) SVCEval-RA - An Evaluation Framework for Adaptive Scalable Video Streaming. In: SourceForge Project. http://sourceforge.net/projects/svceval-ra/ . Accessed 1 May 2015Castellanos W, Guerri JC, Arce P (2015) A QoS-aware routing protocol with adaptive feedback scheme for video streaming for mobile networks. Comput Commun. http://dx.doi.org/10.1016/j.comcom.2015.08.012Castellanos W, Arce P, Acelas P, Guerri JC (2012) Route Recovery Algorithm for QoS-Aware Routing in MANETs. Springer Berlin Heidelberg, Bilbao, pp. 81–93Chikkerur S, Sundaram V, Reisslein M, Karam LJ (2011) Objective video quality assessment methods: A classification, review, and performance comparison. Broadcast, IEEE Trans on 57:165–182Choupani R, Wong S, Tolun M (2014) Multiple description coding for SNR scalable video transmission over unreliable networks. Multimed Tools Appl 69:843–858. doi: 10.1007/s11042-012-1150-9CISCO Corp. (2014) Cisco Visual Networking Index Forecast and Methodology. In: White Paper. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/ip-ngn-ip-next-generation-network/white_paper_c11-481360.pdf.Dai M, Zhang Y, Loguinov D (2009) A unified traffic model for MPEG-4 and H. 264 video traces. IEEE Trans Multimedia 11:1010–1023Detti A, Bianchi G, Pisa C, et al. (2009) SVEF: an open-source experimental evaluation framework for H.264 scalable video streaming. In: IEEE Symposium on Computers and Communications. pp 36–41Espina F, Morato D, Izal M, Magaña E (2014) Analytical model for MPEG video frame loss rates and playback interruptions on packet networks. Multimed Tools Appl 72:361–383. doi: 10.1007/s11042-012-1344-1Fiems D, Steyaert B, Bruneel H (2012) A genetic approach to Markovian characterisation of H.264 scalable video. Multimedia Tools Appl 58:125–146Floyd S, Handley M, Kohler E Datagram Congestion Control Protocol (DCCP). http://tools.ietf.org/html/rfc4340 . Accessed 17 Feb 2014Floyd S, Padhye J, Widmer J TCP Friendly Rate Control (TFRC): Protocol Specification. http://tools.ietf.org/html/rfc5348 . Accessed 17 Feb 2014Fraz M, Malkani YA, Elahi MA (2009) Design and implementation of real time video streaming and ROI transmission system using RTP on an embedded digital signal processing (DSP) platform. In: 2nd International Conference on Computer, Control and Communication, 2009. IC4 2009. pp 1–6ISO/IEC (2014) Information technology - Dynamic adaptive streaming over HTTP (DASH) - Part 1: Media presentation description and segment formats.ITU-T (2013) Rec. H.264 & ISO/IEC 14496-10 AVC. Advanced Video Coding for Generic Audiovisual Services.Ivrlač MT, Choi LU, Steinbach E, Nossek JA (2009) Models and analysis of streaming video transmission over wireless fading channels. Signal Process Image Commun 24:651–665. doi: 10.1016/j.image.2009.04.005Karki R, Seenivasan T, Claypool M, Kinicki R (2010) Performance Analysis of Home Streaming Video Using Orb. In: Proceedings of the 20th International Workshop on Network and Operating Systems Support for Digital Audio and Video. ACM, New York, NY, USA, pp 111–116Ke C-H (2012) myEvalSVC-an Integrated Simulation Framework for Evaluation of H. 264/SVC Transmission. KSII Trans Internet Inf Syst (TIIS) 6:377–392. doi: 10.3837/tiis.2012.01.021Ke C-H, Shieh C-K, Hwang W-S, Ziviani A (2008) An Evaluation Framework for More Realistic Simulations of MPEG Video Transmission. J Inf Sci Eng 24:425–440Klaue J, Rathke B, Wolisz A (2003) Evalvid–A framework for video transmission and quality evaluation. In: Computer Performance Evaluation. Modelling Techniques and Tools. Springer, pp 255–272Le TA, Nguyen H (2014) End-to-end transmission of scalable video contents: performance evaluation over EvalSVC—a new open-source evaluation platform. Multimed Tools Appl 72:1239–1256. doi: 10.1007/s11042-013-1444-6Lie A, Klaue J (2008) Evalvid-RA: trace driven simulation of rate adaptive MPEG-4 VBR video. Multimedia Systems 14:33–50. doi: 10.1007/s00530-007-0110-0Moving Pictures Experts Group and ITU-T Video Coding Experts Group (2011) H. 264/SVC reference software (JSVM 9.19.14) and Manual.Nightingale J, Wang Q, Grecos C (2014) Empirical evaluation of H.264/SVC streaming in resource-constrained multihomed mobile networks. Multimed Tools Appl 70:2011–2035. doi: 10.1007/s11042-012-1219-5Parmar H, Thornburgh M (2012) Real-Time Messaging Protocol (RTMP) Specification. AdobePolitis I, Dounis L, Dagiuklas T (2012) H. 264/SVC vs. H. 264/AVC video quality comparison under QoE-driven seamless handoff. Signal Process Image Commun 27:814–826Pozueco L, Pañeda XG, García R, et al. (2013) Adaptable system based on Scalable Video Coding for high-quality video service. Comput Electr Eng 39:775–789. doi: 10.1016/j.compeleceng.2013.01.015Pozueco L, Pañeda XG, García R, et al. (2014) Adaptation engine for a streaming service based on MPEG-DASH. Multimed Tools Appl 1–20. doi: 10.1007/s11042-014-2034-ySchwarz H, Marpe D, Wiegand T (2007) Overview of the Scalable Video Coding Extension of the H.264/AVC Standard. IEEE Trans Circ Syst Video Technol 17:1103–1120. doi: 10.1109/TCSVT.2007.905532Seo H-Y (2013) An Efficient Transmission Scheme of MPEG2-TS over RTP for a Hybrid DMB System. ETRI J 35:655–665. doi: 10.4218/etrij.13.0112.0124Sohn H, Yoo H, De Neve W, et al. (2010) Full-Reference Video Quality Metric for Fully Scalable and Mobile SVC Content. IEEE Trans Broadcast 56:269–280. doi: 10.1109/TBC.2010.2050628Sousa-Vieira M-E (2011) Suitability of the M/G/∞ process for modeling scalable H.264 video traffic. In: Analytical and Stochastic Modeling Techniques and Applications. Springer, pp 149–158Tanwir S, Perros H (2013) A Survey of VBR Video Traffic Models. IEEE Commun Surv Tutor 15:1778–1802. doi: 10.1109/SURV.2013.010413.00071Tanwir S, Perros HG (2014) VBR Video Traffic Models. Wiley, HobokenThe Network Simulator (NS-2). http://www.isi.edu/nsnam/ns . Accessed 6 Feb 2015Unanue I, Urteaga I, Husemann R, et al. (2011) A Tutorial on H. 264/SVC Scalable Video Coding and its Tradeoff between Quality, Coding Efficiency and Performance. Recent Advances on Video Coding 1–24.Van der Auwera G, David PT, Reisslein M, Karam LJ (2008) Traffic and quality characterization of the H. 264/AVC scalable video coding extension. Adv Multimedia 2008:1Wang Y, Claypool M (2005) RealTracer—Tools for Measuring the Performance of RealVideo on the Internet. Multimed Tools Appl 27:411–430. doi: 10.1007/s11042-005-3757-6Wang Z, Lu L, Bovik AC (2004) Video quality assessment based on structural distortion measurement. Signal Process Image Commun 19:121–132. doi: 10.1016/S0923-5965(03)00076–6Wien M, Schwarz H, Oelbaum T (2007) Performance Analysis of SVC. IEEE Trans Circ Syst for Video Technol 17:1194–1203. doi: 10.1109/TCSVT.2007.905530YUV video repository. ftp://ftp.tnt.uni-hannover.de/pub/svc/testsequences/ . Accessed 10 Jan 201

    Instantly Decodable Network Coding: From Point to Multi-Point to Device-to-Device Communications

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    The network coding paradigm enhances transmission efficiency by combining information flows and has drawn significant attention in information theory, networking, communications and data storage. Instantly decodable network coding (IDNC), a subclass of network coding, has demonstrated its ability to improve the quality of service of time critical applications thanks to its attractive properties, namely the throughput enhancement, delay reduction, simple XOR-based encoding and decoding, and small coefficient overhead. Nonetheless, for point to multi-point (PMP) networks, IDNC cannot guarantee the decoding of a specific new packet at individual devices in each transmission. Furthermore, for device-to-device (D2D) networks, the transmitting devices may possess only a subset of packets, which can be used to form coded packets. These challenges require the optimization of IDNC algorithms to be suitable for different application requirements and network configurations. In this thesis, we first study a scalable live video broadcast over a wireless PMP network, where the devices receive video packets from a base station. Such layered live video has a hard deadline and imposes a decoding order on the video layers. We design two prioritized IDNC algorithms that provide a high level of priority to the most important video layer before considering additional video layers in coding decisions. These prioritized algorithms are shown to increase the number of decoded video layers at the devices compared to the existing network coding schemes. We then study video distribution over a partially connected D2D network, where a group of devices cooperate with each other to recover their missing video content. We introduce a cooperation aware IDNC graph that defines all feasible coding and transmission conflictfree decisions. Using this graph, we propose an IDNC solution that avoids coding and transmission conflicts, and meets the hard deadline for high importance video packets. It is demonstrated that the proposed solution delivers an improved video quality to the devices compared to the video and cooperation oblivious coding schemes. We also consider a heterogeneous network wherein devices use two wireless interfaces to receive packets from the base station and another device concurrently. For such network, we are interested in applications with reliable in-order packet delivery requirements. We represent all feasible coding opportunities and conflict-free transmissions using a dual interface IDNC graph. We select a maximal independent set over the graph by considering dual interfaces of individual devices, in-order delivery requirements of packets and lossy channel conditions. This graph based solution is shown to reduce the in-order delivery delay compared to the existing network coding schemes. Finally, we consider a D2D network with a group of devices experiencing heterogeneous channel capacities. For such cooperative scenarios, we address the problem of minimizing the completion time required for recovering all missing packets at the devices using IDNC and physical layer rate adaptation. Our proposed IDNC algorithm balances between the adopted transmission rate and the number of targeted devices that can successfully receive the transmitted packet. We show that the proposed rate aware IDNC algorithm reduces the completion time compared to the rate oblivious coding scheme

    Multi-user video streaming using unequal error protection network coding in wireless networks

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    In this paper, we investigate a multi-user video streaming system applying unequal error protection (UEP) network coding (NC) for simultaneous real-time exchange of scalable video streams among multiple users. We focus on a simple wireless scenario where users exchange encoded data packets over a common central network node (e.g., a base station or an access point) that aims to capture the fundamental system behaviour. Our goal is to present analytical tools that provide both the decoding probability analysis and the expected delay guarantees for different importance layers of scalable video streams. Using the proposed tools, we offer a simple framework for design and analysis of UEP NC based multi-user video streaming systems and provide examples of system design for video conferencing scenario in broadband wireless cellular networks
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