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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 廖婉君(Wanjiun Liao) | |
| dc.contributor.author | Chiao-Wen Lin | en |
| dc.contributor.author | 林喬文 | zh_TW |
| dc.date.accessioned | 2021-06-15T12:57:00Z | - |
| dc.date.available | 2022-07-30 | |
| dc.date.copyright | 2020-09-22 | |
| dc.date.issued | 2020 | |
| dc.date.submitted | 2020-08-11 | |
| dc.identifier.citation | [1] Cisco, Virtual Networking Index, “Global mobile data traffic forecast update, 2017-2022,” white paper, December 2018, available online at http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/mobile-white-paper-c11-520862.html. [2] M. Zink, R. Sitaraman, and K. Nahrstedt, “Scalable 360° video stream delivery: Challenges, solutions, and opportunities,” Proceedings of the IEEE, vol.107, no. 4, pp. 639–650, 2019. [3] T. M. Porcino, E. W. G. Clua, D. G. Trevisan, C. N. Vasconcelos, and L. Valente, “Minimizing cybersickness in head-mounted display systems: Design guidelines and applications,” 2017 IEEE 5th International Conference on Serious Games and Applications for Health (SeGAH), 2017. [4] J.-Y. Lee, P.-H. Han, L. Tsai, R.-D. Peng, Y.-S. Chen, K.-W. Chen, and Y.-P. Hung, “Estimating the simulator sickness in immersive virtual reality with optical flow analysis,” in SIGGRAPH Asia 2017, 2017. [5] D. N., S. P., and S. A., “Can simulator sickness be avoided? a review on temporal aspects of simulator sickness.” Frontiers in psychology, 2018. [6] G. Wang and A. Suh, “User adaptation to cybersickness in virtual reality: A qualitative study,” in Proceedings of the 27th European Conference on Information Systems (ECIS), 2019. [7] L. Xie, Z. Xu, Y. Ban, X. Zhang, and Z. Guo, “360ProbDASH: Improving QoE of 360 video streaming using tile-based HTTP adaptive streaming,” in Proceedings of the 25th ACM International Conference on Multimedia, 2017, p. 315–323. [8] F. Qian, B. Han, Q. Xiao, and V. Gopalakrishnan, “Flare: Practical viewport adaptive 360-degree video streaming for mobile devices,” in Proceedings of the 24th Annual International Conference on Mobile Computing and Networking, 2018, p. 99–114. [9] M. Xiao, C. Zhou, V. Swaminathan, Y. Liu, and S. Chen, “BAS-360°: Exploring spatial and temporal adaptability in 360-degree videos over HTTP/2,” in IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, 2018. [10] L. Sun, F. Duanmu, Y. Liu, Y. Wang, Y. Ye, H. Shi, and D. Dai, “A two-tier system for on-demand streaming of 360-degree video over dynamic networks,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, vol. 9, no. 1, pp. 43–57, 2019. [11] T. Hosfeld, L. Skorin-Kapov, P. E. Heegaard, M. Varela, and K.-T. Chen, “On additive and multiplicative QoS-QoE models for multiple QoS parameters,” in Proceedings of PQS, 2016. [12] L. Skorin-Kapov, M. Varela, T. Hoßfeld, and K.-T. Chen, “A survey of emerging concepts and challenges for QoE management of multimedia services,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 14, May 2018. [13] J. Nightingale, P. Salva-Garcia, J. M. A. Calero, and Q.Wang, “5G-QoE: QoE modelling for ultra-HD video streaming in 5G networks,” IEEE Transactions on Broadcasting, vol. 64, no. 2, pp. 621–634, 2018. [14] T. Zhao, Q. Liu, and C. W. Chen, “QoE in video transmission: A user experience-driven strategy,” IEEE Communications Surveys Tutorials, vol. 19, no. 1, pp. 285–302, 2017. [15] C.-L. Fan, J. Lee, W.-C. Lo, C.-Y. Huang, K.-T. Chen, and C.-H. Hsu, “Fixation prediction for 360° video streaming in head-mounted virtual reality,” in Proceedings of the 27th Workshop on Network and Operating Systems Support for Digital Audio and Video, 2017, p. 67–72. [16] A. S. Fernandes and S. K. Feiner, “Combating VR sickness through subtle dynamic field-of-view modification,” in 2016 IEEE Symposium on 3D User Interfaces (3DUI), 2016. [17] S. Kim, S. Lee, N. Kala, J. Lee, and W. Choe, “An effective FoV restriction approach to mitigate VR sickness on mobile devices: An effective approach to mitigate VR sickness,” Journal of the Society for Information Display, vol. 26, 2018. [18] K. Carnegie and T. Rhee, “Reducing visual discomfort with HMDs using dynamic depth of field,” IEEE Computer Graphics and Applications, vol. 35, no. 5, pp. 34–41, 2015. [19] D. Heyman and M. Sobel, Stochastic Models in Operations Research Volume II: Stochastic Optimization. Dover Publications, 2003. [20] A. Zare, A. Aminlou, M. M. Hannuksela, and M. Gabbouj, “HEVC-compliant tile-based streaming of panoramic video for virtual reality applications,” in Proceedings of the 24th ACM International Conference on Multimedia, 2016. [21] C. Yeo, H. L. Tan, and Y. H. Tan, “On rate distortion optimization using SSIM,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 7, pp. 1170–1181, 2013. 36 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/50764 | - |
| dc.description.abstract | 隨著各種虛擬實境裝置的出現，360度影片在生活中會有著越來越重要的地位。與傳統影片相比，360度影片串流所需的頻寬消耗為數倍甚至十多倍，因此圖塊(tile)分割式串流被用來有效率的傳輸這類有高頻寬與低延遲需求的360度影片。傳統的圖塊選擇與串流演算法在測量用戶體驗特質(Quality of Experience)時並沒有考慮到一項重要的因素：虛擬實境暈眩(cybersickness)，因此本文制定一個新的最佳化問題：圖塊選擇與暈眩控制(TSCC)，在設計有暈眩減輕方法之360度影片串流系統中，最小化選擇圖塊與使用暈眩減輕方法的成本。為了解決TSCC，我們將圖塊與暈眩減輕方法之參數的選擇建構為馬可夫決策過程(Markov Decision Process)來找到小型案例之最佳解，接著抽取其中的特質來設計有效且快速的演算法：有效圖塊選擇與暈眩減輕演算法(ETSCAA)來解決大型案例。模擬結果顯示在各種頻寬、圖塊數量、視區(viewport)的變化下，我們的演算法皆能勝過傳統演算法。 | zh_TW |
| dc.description.abstract | With the emergence of various virtual reality (VR) devices, 360° videos are guaranteed to play increasingly important roles in the near future. In contrast to traditional 2D videos, the bandwidth requirement for streaming a 360° video is an order of magnitude larger. The tile-based streaming algorithm has been proposed to effectively deliver bandwidth-hungry and delay-restricted 360° videos. However, previous algorithms do not consider cybersickness, one of the significant factors of Quality of Experience (QoE), as part of the QoE metric. In this thesis, we investigate a new optimization problem, named Tile Selection with Cybersickness Control Problem (TSCC), in a tile-based adaptive 360°video streaming system with cybersickness alleviation. We aim to minimize the cost function by choosing the best tile set for prefetching and deciding how to use the cybersickness alleviation methods. To solve TSCC, we model the tile and parameter selection as a Markov Decision Process to find the optimal solutions in small cases. Then we extract the intrinsic ideas behind to devise an efficient and effective heuristic algorithm, named Effective Tile Selection and Cybersickness Alleviation Algorithm (ETSCAA), for large cases. Simulation results manifest that our algorithm can outperform baselines no matter bandwidth condition, number of tiles, or viewport size. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T12:57:00Z (GMT). No. of bitstreams: 1 U0001-1008202022265600.pdf: 3916144 bytes, checksum: 05d434bd60f1da146ab8d6e7e9075b45 (MD5) Previous issue date: 2020 | en |
| dc.description.tableofcontents | Abstract i List of Figures iv List of Tables v 1 Introduction 1 1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Motivation and Challenges . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 System model and Problem formulation 7 2.1 System model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2.1 Transition of the expected viewport distortion . . . . . . . 11 2.2.2 Transition of the user head rotation . . . . . . . . . . . . 12 2.2.3 Transition of the packet queue occupancy . . . . . . . . . 13 2.2.4 Transition of the sickness queue occupancy . . . . . . . . 13 2.3 Optimal policy of MDP . . . . . . . . . . . . . . . . . . . . . . . 14 3 Algorithm 16 3.1 Algorithm ETSCAA . . . . . . . . . . . . . . . . . . . . . . . . 16 3.1.1 Viewport Prediction and Tile Selection . . . . . . . . . . 18 3.1.2 Tile Quality Initialization . . . . . . . . . . . . . . . . . . 19 3.1.3 Tile Quality Enhancement . . . . . . . . . . . . . . . . . 20 3.1.4 Final Action Selection . . . . . . . . . . . . . . . . . . . 21 3.2 Time complexity . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4 Performance Evaluation 23 4.1 Simulation Setting . . . . . . . . . . . . . . . . . . . . . . . . . 23 4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.2.1 Scenario 1: Bandwidth . . . . . . . . . . . . . . . . . . . 24 4.2.2 Scenario 2: Number of tiles . . . . . . . . . . . . . . . . 27 4.2.3 Scenario 3: Original viewport size . . . . . . . . . . . . . 30 5 Conclusion 33 Reference 34 | |
| dc.language.iso | en | |
| dc.title | 考量使用者暈眩之基於圖塊分割的適應性360度全景影音串流 | zh_TW |
| dc.title | Cybersickness-aware tile-based adaptive 360 video streaming | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 108-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 楊得年(De-Nian Yang),陳彥仰(Mike Y. Chen),林嘉文(Chia-Wen Lin),陳炳宇(Bing-Yu Chen) | |
| dc.subject.keyword | 虛擬實境,360 度影片,圖塊分割式串流,虛擬實境暈眩,用戶體驗特質,馬可夫決策過程,演算法, | zh_TW |
| dc.subject.keyword | virtual reality,360° video,tile-based streaming,cybersickness,Quality of Experience,Markov Decision Process,algorithm, | en |
| dc.relation.page | 36 | |
| dc.identifier.doi | 10.6342/NTU202002874 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2020-08-11 | |
| 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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