222,438 research outputs found

    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. 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    A low-complexity psychometric curve-fitting approach for the objective quality assessment of streamed game videos

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    The increasing popularity of video gaming competitions, the so called eSports, has contributed to the rise of a new type of end-user: the passive game video streaming (GVS) user. This user acts as a passive spectator of the gameplay rather than actively interacting with the content. This content, which is streamed over the Internet, can suffer from disturbing network and encoding impairments. Therefore, assessing the user's perceived quality, Le the Quality of Experience (QoE), in real-time becomes fundamental. For the case of natural video content, several approaches already exist that tackle the client-side real-time QoE evaluation. The intrinsically different expectations of the passive GVS user, however, call for new real-time quality models for these streaming services. Therefore, this paper presents a real-time Reduced-Reference (RR) quality assessment framework based on a low-complexity psychometric curve-fitting approach. The proposed solution selects the most relevant, low-complexity objective feature. Afterwards, the relationship between this feature and the ground-truth quality is modelled based on the psychometric perception of the human visual system (HVS). This approach is validated on a publicly available dataset of streamed game videos and is benchmarked against both subjective scores and objective models. As a side contribution, a thorough accuracy analysis of existing Objective Video Quality Metrics (OVQMs) applied to passive GVS is provided. Furthermore, this analysis has led to interesting insights on the accuracy of low-complexity client-based metrics as well as to the creation of a new Full-Reference (FR) objective metric for GVS, i.e. the Game Video Streaming Quality Metric (GVSQM)

    Multilayers fast mode decision algorithm for scalable video coding: design, implementation, and streaming evaluation on IEEE 802.11g wireless LAN

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    Nowadays the advancement of technologies has reached the massive growth and commercial success in multimedia and mobile communication. The advancement and development in video coding technology, together with the increase of storage capacity network infrastructures and computing power are enabling an increasing number of video applications. Scalable video coding (SVC) is the extension of H.264/AVC standard. The features in SVC are also developed from the H.264/AVC standard, so that SVC has more features compared to H.264/AVC standard. This provides higher coding complexity in SVC encoder which causes higher encoding time for SVC. SVC is gaining great interest because of its ability and scalability to adapt in various network conditions. SVC allows partial transmission and decoding of a bitstream. This research deals with fast mode decision algorithm for decreasing encoding time or fastening the mode decision process of the SVC encoder. Moreover, the performance of SVC over wireless network will be evaluated. The simulation tools can be of great help for a better understanding of the streaming analysis over the network. Hence, this research utilizes Scalable Video Evaluation Framework (SVEF). The fast mode decision scheme has been implemented and successfully decreased encoding time with negligible loss of quality and bitrate requirement. The streaming simulation has also been performed using the SVEF simulator. The simulation result shows the proposed fast mode decision algorithm provides time saving up to 45 % while maintaining video quality with negligible PSNR loss. For the streaming video, the received packets on the receiver can be reconstructed on maintained video quality with also negligible PSNR loss

    Evaluation of the MDC and FEC over the quality of service and quality of experience for video distribution in ad hoc networks

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    Mobile ad hoc networks (MANETs) offer an excellent scenario for deploying communication applications because of the connectivity and versatility of this kind of networks. In contrast, the topology is usually extremely dynamic causing high rate of packet loss, so that ensuring a specific Quality of Service (QoS) for real-time video services becomes a hard challenge. In this paper, we evaluate the effect of using Multiple Description Coding (MDC) and Forward Error Correction (FEC) techniques for improving video quality in a multimedia content distribution system. A hybrid architecture using fixed and wireless ad hoc networks is proposed, which enables the use of multipoint-to-point transmission. MDC and FEC mechanisms can be combined with multipath transmission to increase the network efficiency and recover lost packets, improving the overall Quality of Experience (QoE) of the receiver. Simulations have been analyzed paying attention to objective parameters (Peak Signal to Noise Ratio, Packet Delivery Ratio, Decodable Frame Rate and interruptions) and subjective parameters. Results show that MDC increases the probability of packet delivery and FEC is able to recover lost frames and reduce video interruptions in moderate mobility scenarios, resulting in the improvement of video quality and the final user experience.This work was supported by project MIQUEL (TEC2007- 68119-C02-01/TCM) of the Spanish Ministry of Education and Science. The authors would like to thank the Editor and the reviewers for helpful suggestions to improve the quality of this paper.Acelas Delgado, P.; Arce Vila, P.; Guerri Cebollada, JC.; Castellanos Hernández, WE. (2014). Evaluation of the MDC and FEC over the quality of service and quality of experience for video distribution in ad hoc networks. 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International Conference on Digital Telecommunications, pp 65–65Gandikota VR, Tamma BR, Murthy CSR (2008) Adaptive-FEC based packet loss resilience scheme for supporting voice communication over adhoc wireless networks. IEEE Trans Mobile Comput 7:1184–1199Gharavi H (2008) Multi-channel for multihop communication links. International Conference on Telecommunications, pp 1–6Grega M, Janowski L, Leszczuk M, Romaniak P, Papir Z (2008) Quality of experience evaluation for multimedia services. Przegląd Telekomunikacyjny i Wiadomości Telekomunikacyjne 4:142–153Hsieh MY, Huang YM, Chian TC (2007) Transmission of layered video streaming via multi-path on ad hoc networks. Multimed Tool Appl 34:155–177ITU—International Telecommunication Union (2007) Definition of quality of experience (QoE)”, Reference: TD 109rev2 (PLEN/12)ITU-R Recommendation BT.500-12 (2009) Methodology for the subjective assessment of the quality of television pictures. International Telecommunication Union, GenevaITU-T Recommendation P.910 (2000) Subjective video quality assessment methods for multimedia applications. International Telecommunication Union, GenevaKao KL, Ke ChH, Shieh CH (2006) An advanced simulation tool-set for video transmission performance evaluation. IEEE Region 10 Conference, pp 1–40Ke CH et al (2006) A novel realistic simulation tool for video transmission over wireless network. Proceedings of the IEEE International Conference on Sensor Networks, Ubiquitous, and Trsutworthy ComputingKeisuke U, Cheeonn C, Hiroshi I (2008) A study on video performance of multipoint-to-point video streaming with multiple description coding over ad hoc networks. EEJ Trans Electron, Inf Syst 128:1431–1437Kilkki K (2008) Quality of experience in communications ecosystem. J Univers Comput Sci 14:615–624Li A (2007) RTP payload format for generic forward error correction. RFC 5109, Dec. 2007Li J, Blake C, Couto DD, Lee H, Morris R (2001) Capacity of ad hoc wireless networks. 7th Annual International Conference on Mobile Computing and Networking, pp 16–21Liao Y, Gibson JD (2011) Routing-aware multiple description video coding over mobile ad-hoc networks. IEEE Trans Multimed 13:132–142Lindeberg M, Kristiansen S, Plagemann T, Goebel V (2011) Challenges and techniques for video streaming over mobile ad hoc networks. Multimed Syst 17:51–82Mao S et al (2003) Video transport over ad hoc networks: multistream coding with multipath transport. IEEE J Sel Area Comm 21:1721–1737Ni P (2009) Towards Optimal Quality of Experience Via Scalable Video Coding. Mälardalen University Press Licentiate Theses, SwedenPinson MH, Wolf S (2004) A new standardized method for objectively measuring video quality. IEEE Trans Broadcast 50:312–322Rong B, Qian Y, Lu K, Hu RQ, Kadoch M (2010) Multipath routing over wireless mesh networks for multiple description video transmission. IEEE J Sel Area Comm 28:321–331Schierl T, Ganger K, Hellge C, Wiegand T, Stockhammer T (2006) SVC-based multisource streaming for robust video trans- mission in mobile ad hoc networks. IEEE Wireless Comm 13:96–103Schierl T, Stockhammer T, Wiegand T (2007) Mobile video transmission using scalable video coding. IEEE Trans Circ Syst Video Tech 17:1204–1217Schwarz H, Marpe D, Wiegand T (2007) Overview of the scalable video coding extension of the H.264/AVC standard. IEEE Trans Circ Syst Video Tech 17:1103–1120VQEG (2008) Video quality experts group. Available online: http://www.vqeg.orgWang Z et al (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612Wei W, Zakhor A (2004) Robust multipath source routing protocol (RMPSR) for video communication over wireless ad hoc net- works. Proceedings of IEEE International Conference on Multimedia and Expo 2:1379–1382Winkler S, Mohandas P (2008) The evolution of video quality measurement: from PSNR to hybrid metrics. IEEE Trans Broadcast 54:660–668Xunqi Y, Modestino JW, Bajic IV (2005) Performance analysis of the efficacy of packet-level FEC in improving video transport over networks. IEEE International Conference on Image Processing 2:177–180Zink M, Schmitt J, Steinmetz R (2005) Layer-encoded video in scalable adaptive streaming. IEEE Trans Multimed 7:75–8
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