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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏宏宇(Hung-Yu Wei) | |
| dc.contributor.author | Kai-Wen Cheng | en |
| dc.contributor.author | 鄭凱文 | zh_TW |
| dc.date.accessioned | 2021-06-17T04:26:58Z | - |
| dc.date.available | 2018-08-15 | |
| dc.date.copyright | 2018-08-15 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-14 | |
| dc.identifier.citation | [1] 3GPP. Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures. Technical Specification (TS) 36.213, 3rd Generation Partnership Project (3GPP), September 2007. Version 8.0.0.
[2] 3GPP. Evolved Universal Terrestrial Radio Access (E-UTRA); Radio Resource Control (RRC). Technical Specification (TS) 36.331, 3rd Generation Partnership Project (3GPP), December 2007. Version 8.0.0. [3] 3GPP. Evolved Universal Terrestrial Radio Access Network (E- UTRAN); Overall description. Technical Specification (TS) 36.300, 3rd Generation Partnership Project (3GPP), April 2007. Version 8.0.0. [4] 3GPP. ransparent end-to-end Packet-switched Streaming Service (PSS); Progressive Download and Dynamic Adaptive Streaming over HTTP (3GP-DASH). Technical Specification (TS) 36.213, 3rd Generation Partnership Project (3GPP), June 2011. Version 10.0.0. [5] R. Andreas, Rohde, and Schwarz. Understanding downlink power allocation in lte, 2011. [6] M. Assaad. Optimal fractional frequency reuse (ffr) in multicellular ofdma system. In Vehicular Technology Conference, 2008. VTC 2008-Fall. IEEE 68th, pages 1–5. IEEE, 2008. [7] L. Bedogni, M. Di Felice, and L. Bononi. Dynamic segment size selection in http based adaptive video streaming. In Computer Communications Workshops (INFOCOM WKSHPS), 2017 IEEE Conference on, pages 665–670. IEEE, 2017. [8] R. Y. Chang, Z. Tao, J. Zhang, and C.-C. Kuo. A graph approach to dynamic fractional frequency reuse (ffr) in multi-cell ofdma networks. In Communications, 2009. ICC’09. IEEE International Conference on, pages 1–6. IEEE, 2009. [9] Y.-C. Chen, J.-W. Chang, C.-H. Tsai, G.-X. Lin, H.-Y. Wei, and F.-M. Yeh. Maxutility resource allocation for indoor small cell networks. IET Communications, 11(2):267–272, 2017. [10] Y.-C. Chen, J.-W. Chang, and H.-Y. Wei. A multi-level qoe framework for smartphone video streaming applications. In Globecom Workshops (GC Wkshps), 2014, pages 225–230. IEEE, 2014. [11] L. De Cicco, V. Caldaralo, V. Palmisano, and S. Mascolo. Elastic: a client-side controller for dynamic adaptive streaming over http (dash). In Packet Video Workshop (PV), 2013 20th International, pages 1–8. IEEE, 2013. [12] D. I. Forum. Dash.js project. [13] E. Gallo, M. Siller, and J. Woods. An ontology for the quality of experience framework. In Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on, pages 1540–1544. IEEE, 2007. [14] Y. M. Hassan, A. Helmy, and M. M. Rehan. Effect of varying segment size on dash streaming quality for mobile user. In Engineering and Technology (ICET), 2014 International Conference on, pages 1–4. IEEE, 2014. [15] T.-Y. Huang, R. Johari, and N. McKeown. Downton abbey without the hiccups: Buffer-based rate adaptation for http video streaming. In Proceedings of the 2013 ACM SIGCOMM workshop on Future human-centric multimedia networking, pages 9–14. ACM, 2013. [16] T.-Y. Huang, R. Johari, N. McKeown, M. Trunnell, and M. Watson. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. ACM SIGCOMM Computer Communication Review, 44(4):187–198, 2015. [17] O. Issa, F. Speranza, T. H. Falk, et al. Quality-of-experience perception for video streaming services: Preliminary subjective and objective results. In Signal & Information Processing Association Annual Summit and Conference (APSIPA ASC), 2012 Asia-Pacific, pages 1–9. IEEE, 2012. [18] D. Jia, G. Wu, S. Li, G. Y. Li, and X. Zhu. Dynamic soft-frequency reuse with intercell coordination in ofdma networks. In Computer Communications and Networks (ICCCN), 2011 Proceedings of 20th International Conference on, pages 1–6. IEEE, 2011. [19] J. Jiang, V. Sekar, and H. Zhang. Improving fairness, efficiency, and stability in http-based adaptive video streaming with festive. IEEE/ACM Transactions on Networking (TON), 22(1):326–340, 2014. [20] K. Khawam, A. Adouane, S. Lahoud, J. Cohen, and S. Tohme. Game theoretic framework for power control in intercell interference coordination. In Networking Conference, 2014 IFIP, pages 1–8. IEEE, 2014. [21] S. Kumar, S. Kalyani, and K. Giridhar. Optimal design parameters for coverage probability in fractional frequency reuse and soft frequency reuse. IET Communications, 9(10):1324–1331, 2015. [22] S. Kumar, S. Kalyani, and K. Giridhar. Impact of sub-band correlation on sfr and comparison of ffr and sfr. IEEE Transactions on Wireless Communications, 15(8):5156–5166, 2016. [23] Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran. Probe and adapt: Rate adaptation for http video streaming at scale. IEEE Journal on Selected Areas in Communications, 32(4):719–733, 2014. [24] K.-H. Lin, C.-H. Tsai, J.-W. Chang, Y.-C. Chen, H.-Y. Wei, and F.-M. Yeh. Maxthroughput interference avoidance mechanism for indoor self-organizing small cell networks. ICT Express, 3(3):132–136, 2017. [25] Y. Liu, S. Dey, D. Gillies, F. Ulupinar, and M. Luby. User experience modeling for dash video. In Packet Video Workshop (PV), 2013 20th International, pages 1–8. IEEE, 2013. [26] H. G. Msakni and H. Youssef. Ensuring video qoe using http adaptive streaming: Issues and challenges. In Multimedia Computing and Systems (ICMCS), 2016 5th International Conference on, pages 200–205. IEEE, 2016. [27] A. Nagate, D. Ogata, and T. Fujii. Cell edge throughput improvement by base station cooperative transmission control with reference signal interference canceller in lte system. In Vehicular Technology Conference (VTC Spring), 2012 IEEE 75th, pages 1–5. IEEE, 2012. [28] A. Nagate, D. Ogata, and T. Fujii. Experimental evaluation of reference signal interference canceller for multi-bs cooperative transmission control in lte. In Vehicular Technology Conference (VTC Fall), 2012 IEEE, pages 1–5. IEEE, 2012. [29] T. Novlan, J. G. Andrews, I. Sohn, R. K. Ganti, and A. Ghosh. Comparison of fractional frequency reuse approaches in the ofdma cellular downlink. In Global Telecommunications Conference (GLOBECOM 2010), 2010 IEEE, pages 1–5. IEEE, 2010. [30] M. H. Pinson and S. Wolf. A new standardized method for objectively measuring video quality. IEEE Transactions on broadcasting, 50(3):312–322, 2004. [31] M. Qian, W. Hardjawana, Y. Li, B. Vucetic, X. Yang, and J. Shi. Adaptive soft frequency reuse scheme for wireless cellular networks. IEEE Transactions on Vehicular Technology, 64(1):118–131, 2015. [32] U. Sallakh, S. S. Mwanje, and A. Mitschele-Thiel. Multi-parameter q-learning for downlink inter-cell interference coordination in lte son. In Computers and Communication (ISCC), 2014 IEEE Symposium on, pages 1–6. IEEE, 2014. [33] H. Shen, Y. Liu, T. Wang, H. Yang, and L. Sang. Qoe-optimal rate adaptation for http adaptive streaming. In Communications in China (ICCC), 2016 IEEE/CIC International Conference on, pages 1–6. IEEE, 2016. [34] Y. Shuai and T. Herfet. Improving user experience in low-latency adaptive streaming by stabilizing buffer dynamics. In Consumer Communications & Networking Conference (CCNC), 2016 13th IEEE Annual, pages 375–380. IEEE, 2016. [35] Y. Shuai, G. Petrovic, and T. Herfet. Olac: an open-loop controller for low-latency adaptive video streaming. In Communications (ICC), 2015 IEEE International Conference on, pages 6874–6879. IEEE, 2015. [36] I. Sodagar. The mpeg-dash standard for multimedia streaming over the internet. IEEE MultiMedia, 18(4):62–67, 2011. [37] K. Spiteri, R. Urgaonkar, and R. K. Sitaraman. Bola: Near-optimal bitrate adaptation for online videos. In INFOCOM 2016-The 35th Annual IEEE International Conference on Computer Communications, IEEE, pages 1–9. IEEE, 2016. [38] A. Thampi, S. Armour, Z. Fan, and D. Kaleshi. A logistic regression approach to location classification in ofdma-based ffr systems. In World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2013 IEEE 14th International Symposium and Workshops on a, pages 1–9. IEEE, 2013. [39] M. Venkataraman, M. Chatterjee, and S. Chattopadhyay. Evaluating quality of experience for streaming video in real time. In Global Telecommunications Conference, 2009. GLOBECOM 2009. IEEE, pages 1–6. IEEE, 2009. [40] F. Wamser, M. Seufert, P. Casas, R. Irmer, P. Tran-Gia, and R. Schatz. Yomoapp: A tool for analyzing qoe of youtube http adaptive streaming in mobile networks. In Networks and Communications (EuCNC), 2015 European Conference on, pages 239–243. IEEE, 2015. [41] X. Xie, X. Zhang, S. Kumar, and L. E. Li. pistream: Physical layer informed adaptive video streaming over lte. In Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, pages 413–425. ACM, 2015. [42] Z. Xu, G. Y. Li, and C. Yang. Optimal threshold design for ffr schemes in multi-cell ofdma networks. In Communications (ICC), 2011 IEEE International Conference on, pages 1–5. IEEE, 2011. [43] Y. Yang, L. Chen, P. Zhao, and W. Wang. Adaptive power ratio updating algorithm in soft frequency reuse scheme. In Vehicular Technology Conference (VTC Fall), 2013 IEEE 78th, pages 1–5. IEEE, 2013. [44] Y. Yu, E. Dutkiewicz, X. Huang, and M. Mueck. Adaptive power allocation for soft frequency reuse in multi-cell lte networks. In Communications and Information Technologies (ISCIT), 2012 International Symposium on, pages 991–996. IEEE, 2012. [45] T. Zhu, N. Liu, Z. Pan, and X. You. Icic-based small cell on/off schemes for lte-a networks. In Communications and Networking in China (ChinaCom), 2015 10th International Conference on, pages 105–110. IEEE, 2015. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70381 | - |
| dc.description.abstract | 視訊串流是一種非常消耗網路資源的服務，在多重基地台的環境下，基地台間彼此的干擾（稱作細胞間干擾）會造成網路頻寬下降，為了維持視訊串流的服務品質，基地台間的資源管理及干擾抑制是非常重要的，不過只有提升頻寬是不夠的，還需要針對訊串流的應用層特性做考慮。自適性影音串流是近年來最常被使用的視訊串流技術，主要的概念是透過客戶端自行監測網路狀況來挑選適當的影片片段品質做播放，然而在自適性影音串流的演算法中，影片品質挑選僅是由客戶端自己選擇，而且也只透過應用層的參數做決定，沒有來自實體層的資訊，若是能透過伺服器端輔助，則更能適應於當下的網路狀況。在這篇論文中，我們綜合地考慮這兩個層面並提出一個整合式的干擾管理與影片位元率選擇策略來改善自適性影音串流服務於一個有細胞間干擾的環境，並且實作於長期演進技術（LTE）的實測平台上分析其表現。表現衡量是透過一個效用分數函數來做量化分析，結果顯示我們的方法在各個情境下皆大幅提升了自適性影音串流的效用分數。 | zh_TW |
| dc.description.abstract | Video streaming are now becoming a major service in wireless network. However, video streaming is a very consuming service that occupies lots of network bandwidth. In Long Term Evolution (LTE) multi-cellular network topology, the resource reuse factor is usually set to 1, which lets adjacent base stations use same frequency band to increase resources efficiency. However, User Equipments (UE) at cell edge may suffer from high interference from neighbor cells. The shortage of network bandwidth caused by the interference from the neighbor cells becomes an issue called Inter-Cell Interference (ICI). Inter-Cell Interference Coordination (ICIC) was introduced to solve ICI problem by 3GPP [3]. To maintain service quality, the management of radio resources and interference mitigation are crucial for multi-cellular networks. To improve video streaming services, increasing network bandwidth is not enough to enhance video streaming service. Application layer characteristics will be also considered. Dynamic Adaptive Streaming over HTTP (DASH) is a popular video streaming technique in recent years. The main concept is that DASH service clients monitor the network conditions and select the video segments with proper quality. However, the network conditions considered in client-based DASH algorithm are usually application level parameters, such as buffer filled level, rebuffering time, historical throughput, etc. There is no physical layer information in DASH application. If the selection of the video quality can be aided by the physical channel information from server, it will more adapt to current network situation. In this thesis, we jointly consider this two aspects and propose a integral interference management and rate selection strategy procedure to improve DASH service in ICI environment. The performance of our design was verified by real LTE testbed system. The quantification of performance analysis is by a DASH utility score function [25]. The results showed we largely improve the DASH utility in each scenario. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:26:58Z (GMT). No. of bitstreams: 1 ntu-107-R05921075-1.pdf: 7734076 bytes, checksum: 7cc4966c3740ff56b28305d604bca15c (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書i
摘要ii Abstract iii 1 Introduction 1 2 Background and Related Works 3 2.1 Inter-Cell Interference Coordination . . . . . . . . . . . . . . . . . . . . 3 2.2 LTE Resource Block . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.3 Dynamic Adaptive Streaming over HTTP . . . . . . . . . . . . . . . . . 7 3 LTE Testbed 11 3.1 LTE Small Cell Testbed Introduction . . . . . . . . . . . . . . . . . . . . 11 3.2 RB Mask and Power Control Command . . . . . . . . . . . . . . . . . . 13 3.3 DASH Service . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4 Utility-Aware DASH Improvement in ICI Environment 19 4.1 Problem Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.1.1 DASH Utility Function . . . . . . . . . . . . . . . . . . . . . . . 19 4.1.2 System Models and Control Variables . . . . . . . . . . . . . . . 21 4.2 Interference Management . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.2.1 Band Division and Power Control . . . . . . . . . . . . . . . . . 27 4.2.2 Interference Level Classification . . . . . . . . . . . . . . . . . . 32 4.2.3 Exception Handling . . . . . . . . . . . . . . . . . . . . . . . . 34 4.3 Resource Allocation and Rate Selection . . . . . . . . . . . . . . . . . . 35 4.3.1 Resource Allocation . . . . . . . . . . . . . . . . . . . . . . . . 35 4.3.2 Server-Aided Rate Selection . . . . . . . . . . . . . . . . . . . . 37 4.4 Flowchart of Cross-layer Approaches . . . . . . . . . . . . . . . . . . . 39 4.5 Utility-Aware DASH Improvement Procedure . . . . . . . . . . . . . . . 40 4.5.1 Initial Delay Improvement . . . . . . . . . . . . . . . . . . . . . 41 4.5.2 Stall and Quality Variation Improvement . . . . . . . . . . . . . 42 5 Performance Analysis 45 5.1 [Scenario 1] Congestion in center . . . . . . . . . . . . . . . . . . . . . . 45 5.2 [Scenario 2] Interference applied when resources are enough . . . . . . . 49 5.3 [Scenario 3] Interference applied when resources are insufficient . . . . . 50 5.4 [Scenario 4] Power enhanced in edge . . . . . . . . . . . . . . . . . . . . 52 5.5 [Scenario 5] Congestion in edge . . . . . . . . . . . . . . . . . . . . . . 56 5.6 [Scenario 6] Uniform distribution using 4K video content . . . . . . . . . 59 5.7 [Scenario 7] All UEs in DASH service . . . . . . . . . . . . . . . . . . . 64 6 Conclusion 69 Bibliography 70 | |
| dc.language.iso | en | |
| dc.title | 透過干擾管理與影片位元率選擇之改善自適性影音串流於效用感知 | zh_TW |
| dc.title | Utility-Aware DASH Improvement by Interference Management and Rate Selection | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 周敬淳(Ching-Chun Chou),謝秉融(Ping-Jung Hsieh),蔡華龍(Hua-Lung Tsai),王志宇(Chih-Yu Wang) | |
| dc.subject.keyword | 細胞間干擾協調,自適性影音串流,實測平台, | zh_TW |
| dc.subject.keyword | Inter-Cell Interference Coordination (ICIC),Dynamic Adaptive Streaming over HTTP (DASH),Testbed, | en |
| dc.relation.page | 75 | |
| dc.identifier.doi | 10.6342/NTU201803334 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電機工程學研究所 | zh_TW |
| 顯示於系所單位： | 電機工程學系 | |
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