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Towards network-wide QoE fairness using openflow-assisted adaptive video streaming
Open accessAbstract
Video streaming is an increasingly popular way to consume media content. Adaptive video streaming is an emerging delivery technology which aims to increase user QoE and maximise connection utilisation. Many implementations naively estimate bandwidth from a one-sided client perspective, without taking into account other devices in the network. This behaviour results in unfairness and could potentially lower QoE for all clients. We propose an OpenFlow-assisted QoE Fairness Framework that aims to fairly maximise the QoE of multiple competing clients in a shared network environment. By leveraging a Software Defined Networking technology, such as OpenFlow, we provide a control plane that orchestrates this functionality. The evaluation of our approach in a home networking scenario introduces user-level fairness and network stability, and illustrates the optimisation of QoE across multiple devices in a network.
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“With SAND, bandwidth reservation and bitrate guidance strategies have attracted much attention in the multimedia streaming community [7], [107] [198], [196].”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [7]
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“Some of the approaches, for example in [7,198], employ an optimization function that interacts with the controller to dynamically optimize QoE fairness of multiple competing clients by setting the bitrate for each streaming video in the network.”
From QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey (2020) · cites this paper as [7]
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Cites
McKeown (2008) · doi:10.1145/1355734.1355746 · Cited by 8,513
Video quality assessment based on structural distortion measurement
Wang, Z., Lu, L., & Bovik, A. C. (2004) · Signal Processing: Image Communication · doi:10.1016/s0923-5965(03)00076-6 · Cited by 1,024
Improving fairness, efficiency, and stability in HTTP-based adaptive video streaming with FESTIVE
Jiang, J., Sekar, V., & Zhang, H. (2012) · ACM International Conference on emerging Networking EXperiments and Technologies (CoNEXT) · doi:10.1145/2413176.2413189 · Cited by 694
Understanding the impact of video quality on user engagement
Dobrian, F., Sekar, V., Awan, A., Stoica, I. G., Joseph, D. A., Ganjam, A., Zhan, J., & Zhang, H. (2011) · ACM SIGCOMM Conference (SIGCOMM) · doi:10.1145/2018436.2018478 · Cited by 674
A tree-search algorithm for mixed integer programming problems
Dakin, R. J. (1965) · The Computer Journal · doi:10.1093/comjnl/8.3.250 · Cited by 641
An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP
Akhshabi, S., Begen, A. C., & Dovrolis, C. (2011) · doi:10.1145/1943552.1943574 · Cited by 616
Bufferbloat: Dark Buffers in the Internet
Gettys, J. (2011) · IEEE Internet Computing · doi:10.1109/mic.2011.56 · Cited by 600
Huang, T.-Y., Handigol, N., Heller, B., McKeown, N., & Johari, R. (2012) · ACM Internet Measurement Conference (IMC) · doi:10.1145/2398776.2398800 · Cited by 344
What happens when HTTP adaptive streaming players compete for bandwidth?
Akhshabi, S., Anantakrishnan, L., Begen, A. C., & Dovrolis, C. (2012) · doi:10.1145/2229087.2229092 · Cited by 343
Understanding the impact of video quality on user engagement
Dobrian, F., Sekar, V., Awan, A., Stoica, I. G., Joseph, D. A., Ganjam, A., Zhan, J., & Zhang, H. (2011) · ACM SIGCOMM Computer Communication Review · doi:10.1145/2043164.2018478 · Cited by 332
Research works printed but not linked: 1
- Mu M., 2013, LNCS, 302
Cited by
Sorted by citations
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Mu, M., Simpson, S. V., Farshad, A., Ni, Q., & Race, N. (2015) · International Workshop on Quality of Service · doi:10.1109/iwqos.2015.7404718 · Cited by 10
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Wang, Y., Agarwal, M., Lan, T., & Aggarwal, V. (2022) · Algorithms · doi:10.3390/a15070227 · Cited by 10
An SDN Architecture for Privacy-Friendly Network-Assisted DASH
Kleinrouweler, J. W., Cabrero, S., & Cesar, P. S. (2017) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3092838 · Cited by 10
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Atawia, R., Hassanein, H. S., & Noureldin, A. M. (2017) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2017.2777988 · Cited by 10
Adaptive multicast streaming for videoconferences on software-defined networks
Hasrouty, C. A., Lamali, M. L., Autefage, V., Olariu, C., Magoni, D., & Murphy, J. (2018) · Computer Communications · doi:10.1016/j.comcom.2018.09.009 · Cited by 10
SSIM and ML based QoE enhancement approach in SDN context
Letaïfa, A. B. (2019) · Advances in computers · doi:10.1016/bs.adcom.2019.02.004 · Cited by 10
Accurate Network Resource Allocation in SDN according to Traffic Demand
Cui, H., Ma, C., Lai, W., Zheng, L., & Liu, Y. (2015) · International Conference on Mechatronics, Materials, Chemistry and Computer Engineering (ICMMCCE) · doi:10.2991/icmmcce-15.2015.227 · Cited by 10
Assisting Delay and Bandwidth Sensitive Applications in a Self-Driving Network
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Schwarzmann, S., Zinner, T., & Dobrijević, O. (2016) · International Teletraffic Congress · doi:10.1109/itc-28.2016.310 · Cited by 9
Bozkurt, I. N., & Benson, T. (2016) · Symposium on SDN Research · doi:10.1145/2890955.2890972 · Cited by 9
Live streaming of 4K ultra-high definition video over the internet
Petrangeli, S., van der Hooft, J., Wauters, T., Huysegems, R., Alface, P. R., Bostoen, T., & Turck, F. D. (2016) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/2910017.2910627 · Cited by 9
Price-Based Controller for Utility-Aware HTTP Adaptive Streaming
D’Aronco, S., Toni, L., & Pascal Frossard, B. (2017) · IEEE Multimedia · doi:10.1109/mmul.2017.41 · Cited by 9
Mustafa, I. B., Nadeem, T., & Halepovic, E. (2018) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3240508.3240676 · Cited by 9
Silva, S. D., Bruneau‐Queyreix, J., Lacaud, M., Négru, D., & Réveillère, L. (2019) · International Journal of Network Management · doi:10.1002/nem.2081 · Cited by 9
Game Theory Based Dynamic Adaptive Video Streaming for Multi-Client Over NDN
Tan, X., Xu, L., Ni, J., Li, S., Jiang, X., & Zheng, Q. (2021) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2021.3100768 · Cited by 9
Improving User Experience when HTTP Adaptive Streaming Clients Compete for Bandwidth
Guguen, C. T., Bolzer, F. L., & Houdaille, R. (2017) · SMPTE Motion Imaging Journal · doi:10.5594/jmi.2016.2632279 · Cited by 9
On Maximizing QoE in AVC-Based HTTP Adaptive Streaming: An SDN Approach
Erfanian, A., Tashtarian, F., & Yaghmaee, M. H. (2018) · International Workshop on Quality of Service · doi:10.1109/iwqos.2018.8624161 · Cited by 9
Ma, X., Li, Q., Chai, J., Xiao, X., Xia, S., & Jiang, Y. (2019) · International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV) · doi:10.1145/3304112.3325603 · Cited by 9
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Barakabitze, A. A., & Hines, A. (2022) · doi:10.1002/9781119800828.ch5 · Cited by 9
FRT-SDN: an effective firm real time routing for SDN by early removal of late packets
Sedaghat, S., & Jahangir, A. H. (2022) · Telecommunication Systems · doi:10.1007/s11235-022-00913-2 · Cited by 8
Management of virtual network resources for multimedia applications
Gomes, R. L., Bittencourt, L., Madeira, E. R. M., Cerqueira, E., & Gerla, M. (2015) · Multimedia Systems · doi:10.1007/s00530-015-0501-6 · Cited by 8
Design of a layer-based video streaming system over software-defined networks
Kalan, R. S., Çetinkaya, C., & Sayit, M. F. (2017) · International Conference on the Network of the Future (NOF) · doi:10.1109/nof.2017.8251213 · Cited by 8
Silva, S. D., Bruneau‐Queyreix, J., Lacaud, M., Négru, D., & Réveillère, L. (2018) · doi:10.1145/3210424.3210432 · Cited by 8
Ke, W., Wang, Y., Ye, M., & Chen, J. (2021) · IEEE Access · doi:10.1109/access.2021.3084738 · Cited by 8
Evaluation of Shared Resource Allocation Using SAND for ABR Streaming
Pham, S., Heeren, P., Schmidt, C., Silhavy, D., & Arbanowski, S. (2020) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3388926 · Cited by 8
DASH-QoS: A scalable network layer service differentiation architecture for DASH over SDN
Sayit, M. F., Çetinkaya, C., Yıldız, H. U., & Tavlı, B. (2019) · Computer Networks · doi:10.1016/j.comnet.2019.02.015 · Cited by 8
A model for evaluating sharing policies for network-assisted HTTP adaptive streaming
Kleinrouweler, J. W., Cabrero, S., van der Mei, R., & Cesar, P. S. (2016) · Computer Networks · doi:10.1016/j.comnet.2016.03.023 · Cited by 7
Quality of experience-aware enhanced inter-cell interference coordination for self organized HetNet
Morel, M.-L. A., & Randriamasy, S. (2017) · IFIP Wireless and Mobile Networking Conference (WMNC) · doi:10.1109/wmnc.2017.8248841 · Cited by 7
Heterogeneous Multiuser QoE Enhancement Over DASH in SDN Networks
Abar, T., Letaïfa, A. B., & Asmi, S. E. (2020) · Wireless Personal Communications · doi:10.1007/s11277-020-07513-w · Cited by 7
Assisted DASH-aware networking over SDN–CCN architecture
Jmal, R., & Fourati, L. C. (2019) · Photonic Network Communications · doi:10.1007/s11107-019-00835-1 · Cited by 7
Chunk Duration--Aware SDN-Assisted DASH
Ozcelik, I. M., & Ersoy, C. (2019) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3337681 · Cited by 7
Dynamic server selection strategy for multi-server HTTP adaptive streaming services
Bouten, N., Claeys, M., Van Poecke, B., Latre, S., & De Turck, F. (2016) · 2016 12th International Conference on Network and Service Management (CNSM) · doi:10.1109/cnsm.2016.7818403 · Cited by 6
Modelling and performance analysis of application‐aware resource management
Wamser, F., Blenk, A., Seufert, M., Zinner, T., Kellerer, W., & Tran‐Gia, P. (2015) · International Journal of Network Management · doi:10.1002/nem.1894 · Cited by 6
Spending "Quality"' Time with the Web Video
Begen, A. C. (2016) · IEEE Internet Computing · doi:10.1109/mic.2016.49 · Cited by 6
Enhancing and Measuring the Performance in Software Defined Networking
Hossain, M. A., Sheikh, M. N. A., Rahman, S. S. M., Biswas, S., & Arman, M. A. I. (2018) · International journal of Computer Networks & Communications · doi:10.5121/ijcnc.2018.10502 · Cited by 6
A simple yet effective network-assisted signal for enhanced DASH quality of experience
Samain, J., Carofiglio, G., Tortelli, M., & Rossi, D. (2018) · doi:10.1145/3210445.3210447 · Cited by 6
An Objective-Driven On-Demand Network Abstraction for Adaptive Applications
Gao, K., Xiang, Q., Wang, X., Yang, Y. R., & Bi, J. (2019) · IEEE/ACM Transactions on Networking · doi:10.1109/tnet.2019.2899905 · Cited by 6
A Software Defined Network Based Research on Fairness in Multimedia
Al-Mashhadani, A. O. B., Mu, M., & Al-Sherbaz, A. (2019) · doi:10.1145/3347447.3356750 · Cited by 6
Self-citationBegen, A. C. (2021) · Applied Networking Research Workshop (ANRW) · doi:10.1145/3472305.3472886 · Cited by 6
CANE: A Cascade Control Approach for Network-Assisted Video QoE Management
Hosseinzadeh, M., Shankar, K., Apostolaki, M., Ramachandran, J., Adams, S. E., Sekar, V., & Sinopoli, B. (2023) · IEEE Transactions on Control Systems Technology · doi:10.1109/tcst.2023.3267716 · Cited by 6
A Quality-of-Experience Database for Adaptive Omnidirectional Video Streaming
Liu, X., Yan, J., Wan, Z., Fang, Y., & Wang, Z. (2023) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2023.3300529 · Cited by 6
Soft sensors in wireless networking as enablers for SDN based management of content delivery
Tosic, M., Iković, O., & Bošković, D. (2016) · International Convention on Information and Communication Technology, Electronics and Microelectronics · doi:10.1109/mipro.2016.7522205 · Cited by 6
Quantitative comparison of application–network interaction: a case study of adaptive video streaming
Schwarzmann, S., Zinner, T., & Dobrijević, O. (2017) · Quality and User Experience · doi:10.1007/s41233-017-0010-9 · Cited by 5
Price-Based Controller for Quality-Fair HTTP Adaptive Streaming
D’Aronco, S., Toni, L., & Pascal Frossard, B. (2016) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2016.0030 · Cited by 5
Dynamic Server Selection Strategy for Multi-server HTTP Adaptive Streaming Services
Bouten, N., Claeys, M., Poecke, B. V., Latré, S., & Turck, F. D. (2016) · Conference on Network and Service Management · doi:10.5555/3375069.3375079 · Cited by 5
Sieber, C., Schwarzmann, S., Blenk, A., Zinner, T., & Kellerer, W. (2020) · ACM Transactions on Modeling and Performance Evaluation of Computing Systems · doi:10.1145/3381996 · Cited by 5
DiffPerf: Toward Performance Differentiation and Optimization With SDN Implementation
Aljoby, W., Wang, X., Divakaran, D. M., Fu, T. Z. J., Ma, R. T. B., & Harras, K. A. (2023) · IEEE Transactions on Network and Service Management · doi:10.1109/tnsm.2023.3297966 · Cited by 5
QoE-fair Resource Allocation for DASH Video Delivery Systems
Cicco, L. D., Manfredi, G., Mascolo, S., & Palmisano, V. (2019) · doi:10.1145/3347447.3356753 · Cited by 5
Belmoukadam, O., Khokhar, M. J., & Barakat, C. (2019) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm46954.2019.9012672 · Cited by 5
A Multi-Commodity Flow Problem for Fair Resource Allocation in Multi-Path Video Delivery Networks
Cicco, L. D., Manfredi, G., Palmisano, V., & Mascolo, S. (2020) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2020.12.1266 · Cited by 6
Silva, S. D., Bruneau‐Queyreix, J., Lacaud, M., Négru, D., & Réveillère, L. (2018) · ACM Multimedia Systems Conference (MMSys) · doi:10.1145/3204949.3208108 · Cited by 5
Autonomous Control for a Reliable Internet of Services
Ганчев, И., van der Mei, R. D., & van den Berg, H. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-90415-3 · Cited by 5
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Ferreira, I. F., Peixoto, M. L. M., & Figueiredo, G. B. (2024) · Computer Communications · doi:10.1016/j.comcom.2024.03.015 · Cited by 4
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Belmoukadam, O., Khokhar, M. J., & Barakat, C. (2020) · International Journal of Network Management · doi:10.1002/nem.2128 · Cited by 4
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Natalino, C., Ristov, S., Wosinska, L., & Furdek, M. (2020) · Computer communications and networks · doi:10.1007/978-3-030-44685-7_2 · Cited by 4
Shimokawa, S., Taenaka, Y., Tsukamoto, K., & Lee, M. J. (2021) · IEEE Access · doi:10.1109/access.2021.3061488 · Cited by 4
QoE-Oriented Fairness Control for DASH Systems Based on the Hierarchical Structure of SVC Streams
Przyłucki, S., Czerwiński, D., & Sierszeń, A. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-39207-3_16 · Cited by 4
Zahran, A. H., Quinlan, J. J., Ramakrishnan, K. K., & Sreenan, C. J. (2018) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3219750 · Cited by 4
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Çetinkaya, C., Herguner, K., Hellge, C., & Sayit, M. F. (2020) · International Journal of Network Management · doi:10.1002/nem.2102 · Cited by 4
Routing Tensor Model with Providing Multimedia Quality
Yevdokymenko, M. (2019) · International Scientific-Practical Conference Problems of Infocommunications Science and Technology · doi:10.1109/picst47496.2019.9061280 · Cited by 4
Hardware-efficient implementation of WFQ algorithm on NetFPGA-based OpenFlow switch
Hong, H. T., Xuan, Q. B., Van, D. D., Ngoc, N. P., & Huu, T. N. (2016) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc.2016.7764821 · Cited by 4
Clustering and Flow Conservation Monitoring Tool for Software Defined Networks
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Metzger, F., Hoßfeld, T., Skorin‐Kapov, L., Haddad, Y., Liotou, E., Počta, P., Melvin, H., Siris, V. A., Žgank, A., & Jarschel, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-90415-3_2 · Cited by 4
Structured Reinforcement Learning for Media Streaming at the Wireless Edge
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QoE-aware optimization for SVC-based adaptive streaming in D2D communications
Hao, L., Jin, C., Gao, X., Kong, L., Wu, F., & Chen, G. (2017) · IEEE International Performance, Computing, and Communications Conference · doi:10.1109/pccc.2017.8280470 · Cited by 3
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Lyko, T., Elkhatib, Y., Sparks, M., Ramdhany, R., & Race, N. (2022) · International Conference on Quality of Multimedia Experience (QoMEX) · doi:10.1109/qomex55416.2022.9900905 · Cited by 3
Self-citationQoE optimization for adaptive streaming with multiple VBR videos
Nguyen, T., Vu, T., Nguyen, D. V., Ngoc, N. P., & Thang, T. C. (2015) · International Conference on Computing, Management and Telecommunications · doi:10.1109/commantel.2015.7394285 · Cited by 3
Go Green with EnVI: the Energy-Video Index
Ejembi, O., & Bhatti, S. N. (2015) · IEEE International Symposium on Multimedia (ISM) · doi:10.1109/ism.2015.50 · Cited by 3
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Yamansavascilar, B., Baktır, A. C., Özgövde, A., & Ersoy, C. (2019) · IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) · doi:10.1109/infcomw.2019.8845205 · Cited by 3
Hemmati, M. (2017) · uO Research (University of Ottawa) · doi:10.20381/ruor-20906 · Cited by 3
QoE-aware dynamic virtual network resource adaptation for EaaS environment
Gomes, R. L., Bittencourt, L. F., Madeira, E. R. M., Cerqueira, E., & Gerla, M. (2015) · International Conference on Communications · doi:10.1109/icc.2015.7249415 · Cited by 2
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Kleinrouweler, J. W., Cabrero, S., van der Mei, R., & Cesar, P. S. (2016) · International Teletraffic Congress · doi:10.1109/itc-28.2016.123 · Cited by 2
Ensuring High QoE for DASH-Based Clients Using Deterministic Network Calculus in SDN Networks
Marai, O. E., Prados-Garzon, J., Bagaa, M., & Taleb, T. (2019) · Globecom · doi:10.1109/globecom38437.2019.9013633 · Cited by 2
In-network adaptation of SHVC video in software-defined networks
Awobuluyi, O., Nightingale, J., Wang, Q., Calero, J. M. A., & Grecos, C. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2227550 · Cited by 2
Seacat: an sdn end-to-end containment architecture
Kano, M. (2016) · doi:10.26053/0h-gp35-j800 · Cited by 2
SDQ: Enabling rapid QoE experimentation using Software Defined Networking
Fawcett, L., Mu, M., Broadbent, M. T. M., Hart, N., & Race, N. (2017) · IFIP/IEEE International Symposium on Integrated Network Management (IM) · doi:10.23919/inm.2017.7987353 · Cited by 2
Self-citationDistributed-collaborative managed dash video services
Sahin, K. E., Bagci, K. T., & Tekalp, A. M. (2017) · International Conference on Network and Service Management (CNSM) · doi:10.23919/cnsm.2017.8256001 · Cited by 2
Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
Kim, K., Kwon, D., Kim, J., & Mohaisen, A. (2019) · Applied Sciences · doi:10.3390/app9112297 · Cited by 2
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Mazur, I., Rak, J., & Nowicki, K. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-50719-0_10 · Cited by 2
Othman, M., Chen, K., & Mokraoui, A. (2020) · Signal Processing: Algorithms, Architectures, Arrangements, and Applications · doi:10.23919/spa50552.2020.9241250 · Cited by 2
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Nguyen, L. H., Lee, A., & Ra, I. (2018) · ACM Conference on Research in Adaptive and Convergent Systems (RACS) · doi:10.1145/3264746.3264794 · Cited by 2
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Лемешко, О. В., Yevdokymenko, M., & Yeremenko, O. (2020) · IEEE International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET) · doi:10.1109/tcset49122.2020.235441 · Cited by 2
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Design and Realization of Cognitive Routing Resources Using Big Data Analysis in SDN
Cui, H., Zhang, Y., Ma, C., Lai, W., Beaulieu, N. C., Sobolevsky, S. L., & Liu, Y. (2015) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2015.69 · Cited by 1
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Cerqueira, E., dos Santos, A. L., do Rosário, D. L., Braun, T. I., & Gerla, M. (2014) · Bern Open Repository and Information System (University of Bern) · doi:10.7892/boris.47845 · Cited by 1
Cloud live streaming System based on Auto-adaptive Overlay for Cyber Physical Infrastructure
Mocanu, B., Mureean, V., Mocanu, M., & Cristea, V. (2016) · doi:10.1145/2962564.2962571 · Cited by 1
Restless Video Bandits: Optimal SVC Streaming in a Multi-User Wireless Network
Hosseini, S. A., & Panwar, S. S. (2019) · IEEE Access · doi:10.1109/access.2019.2947902 · Cited by 1
Partial overlapping chunk based dual-path transmission: Scheme and modelling
Zhang, J., Li, C., Zhao, Y., & Zhang, B. (2017) · International Conference on Communications · doi:10.1109/icc.2017.7996446 · Cited by 1
QoS and QoE evaluations of 2K and 4K video distribution using MPEG-DASH
Nagashima, T., Kanai, K., & Katto, J. (2017) · IEEE Global Conference on Consumer Electronics (GCCE) · doi:10.1109/gcce.2017.8229350 · Cited by 1
Remy, C., Bates, O., Thomas, V., & Broadbent, M. T. M. (2018) · EPiC series in computing · doi:10.29007/kln5 · Cited by 1
Self-citationQoE Control for Dynamic Adaptive Video Streaming Over HTTP at Access Point
Yuan, Y., Lin, S., & Zhou, G. (2019) · IEEE International Conference on Industrial Internet (ICII) · doi:10.1109/icii.2019.00053 · Cited by 1
Determining QoE in Real Time from Video Streaming on SDN Networks
Ternera, M., Solís, L., Peña, J. A. C., & Jabba, D. (2022) · IEEE ANDESCON · doi:10.1109/andescon56260.2022.9989831 · Cited by 1
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Ravindran, K., Cherian, A., & Adiththan, A. (2018) · ACM Symposium on QoS and Security for Wireless and Mobile Networks (Q2SWinet) · doi:10.1145/3267129.3267142 · Cited by 1
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Al-Issa, A. E., Bentaleb, A., Zinner, T., Mkwawa, I.-H. M., & Ghita, B. (2019) · Conference on Innovation in Clouds, Internet and Networks and Workshops (ICIN) · doi:10.1109/icin.2019.8685894 · Cited by 1
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Khan, K., & Goodridge, W. S. (2019) · International Journal of Advanced Networking and Applications · doi:10.35444/ijana.2019.11028 · Cited by 1
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Thinh, P. H., Nam, P. N., Thanh, N. H., & Hương, T. T. (2019) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc46691.2019.8939834 · Cited by 1
Metadata
- Authors
- Georgopoulos, P., Elkhatib, Y., Broadbent, M., Mu, M., & Race, N.
- Affiliations
- Lancaster University (GB)
- Venue
- Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking
- Publisher
- ACM
- Published
- 2013-08-16
- Pages
- 15-20
- OpenAlex
- W2146232773
- Cited by
- 242
- References
- 23
- Citations by year
- 2025: 3 · 2024: 3 · 2023: 6 · 2022: 8 · 2021: 12 · 2020: 18 · 2019: 30 · 2018: 37 · 2017: 43 · 2016: 35 · 2015: 33 · 2014: 13 · 2013: 1
- FWCI
- 20.95
- Type
- conference paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Lancaster EPrints (Lancaster University) (submittedVersion)
- Research Output (Edinburgh Napier University) (submittedVersion)
- Edinburgh Napier Research Repository (Edinburgh Napier University) (submittedVersion)
- Funders
- European Commission (258365, 318343)
- Natural Environment Research Council
- FP7 Information and Communication Technologies
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Keywords
- adaptive video streaming, OpenFlow, software-defined networking, bandwidth estimation, home network, control plane, network stability
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