{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T19:35:20Z","timestamp":1722713720850},"reference-count":40,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2019,7,15]],"date-time":"2019-07-15T00:00:00Z","timestamp":1563148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"There are few network resources in wireless multimedia sensor networks (WMSNs). Compressing media data can reduce the reliance of user\u2019s Quality of Experience (QoE) on network resources. Existing video coding software, such as H.264 and H.265, focuses only on spatial and short-term information redundancy. However, video usually contains redundancy over a long period of time. Therefore, compressing video information redundancy with a long period of time without compromising the user experience and adaptive delivery is a challenge in WMSNs. In this paper, a semantic-aware super-resolution transmission for adaptive video streaming system (SASRT) for WMSNs is presented. In the SASRT, some deep learning algorithms are used to extract video semantic information and enrich the video quality. On the multimedia sensor, different bit-rate semantic information and video data are encoded and uploaded to user. Semantic information can also be identified on the user side, further reducing the amount of data that needs to be transferred. However, identifying semantic information on the user side may increase the computational cost of the user side. On the user side, video quality is enriched with super-resolution technologies. The major challenges faced by SASRT include where the semantic information is identified, how to choose the bit rates of semantic and video information, and how network resources should be allocated to video and semantic information. The optimization problem is formulated as a complexity-constrained nonlinear NP-hard problem. Three adaptive strategies and a heuristic algorithm are proposed to solve the optimization problem. Simulation results demonstrate that SASRT can compress video information redundancy with a long period of time effectively and enrich the user experience with limited network resources while simultaneously improving the utilization of these network resources.<\/jats:p>","DOI":"10.3390\/s19143121","type":"journal-article","created":{"date-parts":[[2019,7,16]],"date-time":"2019-07-16T06:23:16Z","timestamp":1563258196000},"page":"3121","source":"Crossref","is-referenced-by-count":8,"title":["SASRT: Semantic-Aware Super-Resolution Transmission for Adaptive Video Streaming over Wireless Multimedia Sensor Networks"],"prefix":"10.3390","volume":"19","author":[{"given":"Jia","family":"Guo","sequence":"first","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Xiangyang","family":"Gong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Wendong","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Xirong","family":"Que","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]},{"given":"Jingyu","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,7,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7792","DOI":"10.1109\/JSEN.2016.2601258","article-title":"Energy aware and adaptive cross-layer scheme for video transmission over wireless sensor networks","volume":"16","author":"Youssif","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1109\/TMM.2016.2645398","article-title":"Joint Compression of Near-Duplicate Videos","volume":"19","author":"Wang","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"746","DOI":"10.1109\/TMC.2017.2739744","article-title":"A Joint Framework for QoS and QoE for Video Transmission over Wireless Multimedia Sensor Networks","volume":"17","author":"Usman","year":"2018","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2693","DOI":"10.1007\/s11276-015-0939-7","article-title":"ACWSN: An adaptive cross layer framework for video transmission over wireless sensor networks","volume":"21","author":"Youssif","year":"2015","journal-title":"Wirel. Netw."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., and Wang, Z. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., and Change Loy, C. (2018, January 18\u201322). Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00070"},{"key":"ref_7","unstructured":"Kaiming, H., Georgia, G., Piotr, D., and Ross, G. (2017, January 22\u201329). Mask R-cnn. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1666","DOI":"10.1109\/TII.2017.2783439","article-title":"Learning-Based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities","volume":"14","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1109\/COMST.2014.2387836","article-title":"A Survey of Incentive Mechanisms for Participatory Sensing","volume":"17","author":"Gao","year":"2015","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_10","first-page":"2169","article-title":"Mutual Information Maximization based Collaborative Data Collection with Calibration Constraint","volume":"7","author":"Zhang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tian, Y., Tang, Z., and Ma, J. (2019). Sparse sensing data-based participant selection for people finding. Int. J. Distrib. Sens. Netw., 15.","DOI":"10.1177\/1550147719844930"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3321","DOI":"10.1109\/ACCESS.2018.2793228","article-title":"Improvement of JPEG XT Floating-Point HDR Image Coding Using Region Adaptive Prediction","volume":"6","author":"Kwon","year":"2018","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Tang, M., Zhang, Y., Wen, J., and Yang, S. (2017, January 10\u201314). Optimized video coding for omnidirectional videos. Proceedings of the 2017 IEEE International Conference on Multimedia and Expo, ICME 2017, Hong Kong, China.","DOI":"10.1109\/ICME.2017.8019460"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1859","DOI":"10.1109\/TCSVT.2015.2473296","article-title":"Novel Bitrate Saving and Fast Coding for Depth Videos in 3D-HEVC","volume":"26","author":"Chung","year":"2016","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"16406","DOI":"10.1109\/ACCESS.2017.2739343","article-title":"Mobile Edge Computing Enhanced Adaptive Bitrate Video Delivery with Joint Cache and Radio Resource Allocation","volume":"5","author":"Xu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1109\/TCSVT.2016.2539618","article-title":"Green video transmission in the mobile cloud networks","volume":"27","author":"Lin","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1816","DOI":"10.1109\/JSAC.2018.2845000","article-title":"QoE-Assured 4K HTTP Live Streaming via Transient Segment Holding at Mobile Edge","volume":"36","author":"Ge","year":"2018","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Graf, M., Timmerer, C., and Mueller, C. (2017, January 20\u201323). Towards bandwidth efficient adaptive streaming of omnidirectional video over http: Design, implementation, and evaluation. Proceedings of the 8th ACM on Multimedia Systems Conference, Taipei, Taiwan.","DOI":"10.1145\/3083187.3084016"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"23233","DOI":"10.1109\/ACCESS.2017.2732358","article-title":"Quantum Search-Aided Multi-User Detection of IDMA-Assisted Multi-Layered Video Streaming","volume":"5","author":"Botsinis","year":"2017","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"43978","DOI":"10.1109\/ACCESS.2018.2859812","article-title":"Quality-Driven Resource Allocation for Wireless Video Transmissions Under Energy Efficiency and Delay Constraints","volume":"6","author":"Ye","year":"2018","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TMM.2016.2601020","article-title":"SRLSP: A face image super-resolution algorithm using smooth regression with local structure prior","volume":"19","author":"Jiang","year":"2017","journal-title":"IEEE Trans. Multimed."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"5438","DOI":"10.1109\/ACCESS.2018.2793847","article-title":"Improved Imaging Performance in Super-Resolution Localization Microscopy by YALL1 Method","volume":"6","author":"Zhao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_23","unstructured":"Wang, S., Zhang, L., Liang, Y., and Pan, Q. (2012, January 16\u201321). Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Providence, Rhode Island."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., and Mu Lee, K. (2016, January 27\u201330). Deeply-recursive convolutional network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.181"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Shi, W., Caballero, J., Husz\u00e1r, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., and Wang, Z. (2016, January 27\u201330). Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.207"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"10966","DOI":"10.1109\/ACCESS.2018.2797197","article-title":"High Dynamic Range and Super-Resolution Imaging From a Single Image","volume":"6","author":"Park","year":"2018","journal-title":"IEEE Access"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"33421","DOI":"10.1109\/ACCESS.2018.2846363","article-title":"Dual-Streams Edge Driven Encoder-Decoder Network for Image Super-Resolution","volume":"6","author":"Li","year":"2018","journal-title":"IEEE Access"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"23767","DOI":"10.1109\/ACCESS.2018.2829908","article-title":"Video Super-Resolution via Residual Learning","volume":"6","author":"Wang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ran, X., Chen, H., Zhu, X., Liu, Z., and Chen, J. (2018, January 15\u201319). DeepDecision: A Mobile Deep Learning Framework for Edge Video Analytics. Proceedings of the IEEE International Conference on Computer Communications(infocom), Honolulu, HI, USA.","DOI":"10.1109\/INFOCOM.2018.8485905"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Caballero, J., Ledig, C., Aitken, A.P., Acosta, A., Totz, J., Wang, Z., and Shi, W. (2017, January 21\u201326). Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition(CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.304"},{"key":"ref_32","unstructured":"Index, C.V.N. (2019, July 12). Global Mobile Data Traffic Forecast Update, 2017\u20132022. Available online: https:\/\/www.cisco.com\/c\/en\/us\/solutions\/collateral\/service-provider\/visual-networking-index-vni\/white-paper-c11-738429.html#_Toc953327."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"791","DOI":"10.1109\/TBC.2018.2832444","article-title":"An Optimized Hybrid Unicast\/Multicast Adaptive Video Streaming Scheme Over MBMS-Enabled Wireless Networks","volume":"64","author":"Guo","year":"2018","journal-title":"IEEE Trans. Broadcast."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Raca, D., Quinlan, J.J., Zahran, A.H., and Sreenan, C.J. (2018, January 12\u201315). Beyond Throughput: A 4G LTE Dataset with Channel and Context Metrics. Proceedings of the ACM Multimedia Systems Conference(MMSys), Amsterdam, The Netherlands.","DOI":"10.1145\/3204949.3208123"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sajjadi, M.S., Schlkopf, B., and Hirsch, M. (2017, January 22\u201329). Enhancenet: Single image super-resolution through automated texture synthesis. Proceedings of the Computer Vision (ICCV), IEEE International Conference on IEEE, Venice, Italy.","DOI":"10.1109\/ICCV.2017.481"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kim, J., Kwon Lee, J., and Mu Lee, K. (2016, January 27\u201330). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.182"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., and Yang, M.H. (2017, January 21\u201326). Deep laplacian pyramid networks for fast and accurate superresolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.image.2017.05.015","article-title":"QoE-driven optimization for cloud-assisted DASH-based scalable interactive multiview video streaming over wireless network","volume":"57","author":"Zhao","year":"2017","journal-title":"Signal Process. Image Commun."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cviu.2016.12.009","article-title":"Learning a No-Reference Quality Metric for Single-Image Super-Rolution","volume":"158","author":"Ma","year":"2017","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wei, X., Du, J., Xue, Z., Liang, M., Geng, Y., Xu, X., and Lee, J. (2019). A Very Deep Two-stream Network for Crowd Type Recognition. Neurocomputing.","DOI":"10.1016\/j.neucom.2018.10.106"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3121\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,19]],"date-time":"2024-06-19T04:01:42Z","timestamp":1718769702000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/14\/3121"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,15]]},"references-count":40,"journal-issue":{"issue":"14","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["s19143121"],"URL":"http:\/\/dx.doi.org\/10.3390\/s19143121","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,7,15]]}}}
