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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 魏宏宇(Hung-Yu Wei) | |
| dc.contributor.author | Tse-Yu Chen | en |
| dc.contributor.author | 陳則宇 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:09:50Z | - |
| dc.date.available | 2021-11-03 | |
| dc.date.available | 2022-11-24T03:09:50Z | - |
| dc.date.copyright | 2021-11-03 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-27 | |
| dc.identifier.citation | [1] Openstack official website. https://www.openstack.org/. Accessed: 2021-06-05. [2] Onap official website. https://www.onap.org/, Dec 2020. [3] U. Bulkan, T. Dagiuklas, M. Iqbal, K. M. S. Huq, A. Al-Dulaimi, and J. Rodriguez. On the load balancing of edge computing resources for on-line video delivery. IEEE Access, 6:73916–73927, 2018. [4] C. Jiang, T. Cao, C. Li, X. Wang, and Y. Zhang. Multimedia resources purchase and distribution optimization for 5g edge cloud networks. IEEE Access, 8:59340–59350, 2020. [5] L. Jiao, H. Yin, and Y. Wu. Dynamic resource allocation for scalable video streaming in ofdma wireless networks. IEEE Access, 8:33489–33499, 2020. [6] M. Kim and K. Chung. Edge computing assisted adaptive streaming scheme for mobile networks. IEEE Access, 9:2142–2152, 2021. [7] W.-J. Kim, K.-N. Joo, and C.-H. Youn. Short-term time-varying request model based chunk caching scheme for live streaming in mobile edge-cloud environment. IEEE Access, 7:177148–177163, 2019. [8] C. Liu, H. Zhang, H. Ji, and X. Li. Mec-assisted flexible transcoding strategy for adaptive bitrate video streaming in small cell networks. China Communications, 18(2):200–214, 2021. [9] D. Liu, Z. Wang, and J. Zhang. Video stream distribution scheme based on edge computing network and user interest content model. IEEE Access, 8:30734–30744, 2020. [10] Y. Liu, S. Dey, D. Gillies, F. Ulupinar, and M. Luby. User experience modeling for dash video. In 2013 20th International Packet Video Workshop, pages 1–8, 2013. [11] A. Mehrabi, M. Siekkinen, and A. Ylä-Jaaski. Qoe-traffic optimization through collaborative edge caching in adaptive mobile video streaming. IEEE Access, 6:52261– 52276, 2018. [12] W. U. Rahman, C. S. Hong, and E.-N. Huh. Edge computing assisted joint quality adaptation for mobile video streaming. IEEE Access, 7:129082–129094, 2019. [13] S. Rezvani, S. Parsaeefard, N. Mokari, M. R. Javan, and H. Yanikomeroglu. Cooperative multi-bitrate video caching and transcoding in multicarrier noma-assisted heterogeneous virtualized mec networks. IEEE Access, 7:93511–93536, 2019. [14] A.-T. Tran, N.-N. Dao, and S. Cho. Bitrate adaptation for video streaming services in edge caching systems. IEEE Access, 8:135844–135852, 2020. [15] L. Van Ma, V. Q. Nguyen, J. Park, and J. Kim. Nfv-based mobile edge computing for lowering latency of 4k video streaming. In 2018 Tenth International Conference on Ubiquitous and Future Networks (ICUFN), pages 670–673, 2018. [16] W. Wu, Y. Gao, T. Zhou, Y. Jia, H. Zhang, T. Wei, and Y. Sun. Deep reinforcement learning-based video quality selection and radio bearer control for mobile edge computing supported short video applications. IEEE Access, 7:181740–181749, 2019. [17] X. Xu, J. Liu, and X. Tao. Mobile edge computing enhanced adaptive bitrate video delivery with joint cache and radio resource allocation. IEEE Access, 5:16406– 16415, 2017. [18] S. Yuan, J. Li, C. Wu, Y. Ji, and Y. Zhang. Dcvp: Distributed collaborative video stream processing in edge computing. In 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS), pages 625–632, 2020. [19] K. Zahoor, K. Bilal, A. Erbad, A. Mohamed, and M. Guizani. Multicast at edge: An edge network architecture for service-less crowdsourced live video multicast. IEEE Access, 9:59508–59526, 2021. [20] C. Zhang, H. Pang, J. Liu, S. Tang, R. Zhang, D. Wang, and L. Sun. Toward edgeassisted video content intelligent caching with long short-term memory learning. IEEE Access, 7:152832–152846, 2019. [21] Z. Zhang, J. Dai, M. Zeng, D. Liu, and S. Mao. Scalable video caching for information centric wireless networks. IEEE Access, 8:77272–77284, 2020. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80579 | - |
| dc.description.abstract | 隨著近年來影片流量的不斷增長，維持一個具有高可靠性和服務質量的影片流媒體系統一直是一個熱門話題。Edge/Fog 計算和DASH/HLS等新興技術的出現，有可能縮短延遲，減少停滯時間，提高用戶體驗。然而，資源有限的特性使得Edge/Fog 系統與傳統的雲計算系統相比，必須更加重視資源管理和分配。目前，很少有論文涉及完整的直播系統和三種典型資源的分配，其中大部分在架構和消息流上都不統一。本文提出了一個與最新的P1935標準相一致的Edge/Fog系統，以及一個基於Edge/Fog的影片串流媒體服務，包括直播串流和傳統的按需影片串流。同時，我們設計了幾個相應的資源管理機制來提高系統的性能，將所有的計算、網絡和存儲資源考慮在內。最後，我們在測試平台上實現了我們提出的 方案，根據我們的實驗結果，我們的系統和機制為觀眾提供了更好的QoE和更高的可靠性。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:09:50Z (GMT). No. of bitstreams: 1 U0001-2410202123292500.pdf: 3275613 bytes, checksum: 5c9a0a983a3e62aec8285264458f0562 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | 口試委員審定書 ... i 摘要 ... ii Abstract ... iii Contents ... v List of Figures ... vii List of Tables ... ix Chapter 1 Introduction ... 1 Chapter 2 Related Work ... 4 Chapter 3 Edge/Fog-based Video Streaming Service ... 8 3.1 Edge/Fog System Architecture . . . . . . . . . . . . . . . . . . . . . 8 3.2 Edge/Fog System-based Video Streaming Service . . . . . . . . . . . 11 Chapter 4 Resource Management Framework ... 14 4.1 Server Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.2 Real-time Dynamic Transcoding Decision . . . . . . . . . . . . . . . 16 4.3 Dynamic Network Configuration . . . . . . . . . . . . . . . . . . . 17 4.4 Collaborative Caching . . . . . . . . . . . . . . . . . . . . . . . . . 18 4.5 On-demand DASH-ify . . . . . . . . . . . . . . . . . . . . . . . . . 19 Chapter 5 Formulation ... 20 5.1 Objective Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5.2 QoE Utility Function . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Chapter 6 Evaluation Methodology ... 27 6.1 Testbed Implementation . . . . . . . . . . . . . . . . . . . . . . . . 27 Chapter 7 Evaluation Results ... 31 7.0.1 Real-time Dynamic Transcoding Decision . . . . . . . . . . . . . . 32 7.0.2 Dynamic Network Configuration . . . . . . . . . . . . . . . . . . . 35 7.0.3 Collaborative Caching . . . . . . . . . . . . . . . . . . . . . . . . 38 7.0.4 On-demand DASH-ify . . . . . . . . . . . . . . . . . . . . . . . . 39 7.0.5 The Joint Resource Management . . . . . . . . . . . . . . . . . . . 39 Chapter 8 Conclusions ... 44 References ... 45 | |
| dc.language.iso | en | |
| dc.subject | HLS | zh_TW |
| dc.subject | 邊緣運算 | zh_TW |
| dc.subject | 影片串流 | zh_TW |
| dc.subject | 資源管理 | zh_TW |
| dc.subject | DASH | zh_TW |
| dc.subject | resource management | en |
| dc.subject | HLS | en |
| dc.subject | DASH | en |
| dc.subject | video streaming | en |
| dc.subject | Edge/Fog computing | en |
| dc.title | 基於邊緣運算的影片直播串流系統之資源管理框架 | zh_TW |
| dc.title | Resource Management in Edge/Fog-based Video Streaming System | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 謝宏昀(Hsin-Tsai Liu),曹孝櫟(Chih-Yang Tseng),王志宇 | |
| dc.subject.keyword | 邊緣運算,影片串流,資源管理,DASH,HLS, | zh_TW |
| dc.subject.keyword | Edge/Fog computing,video streaming,resource management,DASH,HLS, | en |
| dc.relation.page | 47 | |
| dc.identifier.doi | 10.6342/NTU202104100 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-27 | |
| 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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