我們使用QUIC / UDP協議設計，設計、實作、評估用於360度視頻的方塊DASH串流媒體系統， 其中利用多路復用和優先串流來發送即將錯過其播出時間的緊急方塊頻。 我們設計了一個新架構，可以同時向伺服器端利用較低優的順位索取常規方塊視頻和較高順位索取的緊急方塊視頻，讓單個QUIC連線上有多個串流。 我們為這個新架構設計了幾個核心組件，包括眼球注視預測演算法、快速方塊視頻挑選器、動態調整視頻畫質(ABR)的演算法。 與串流2D平面視頻相比，使用DASH將360度方塊視頻傳輸到頭戴式顯示器更具挑戰性。 據我們所知，大多數現有的DASH的ABR演算法不是針對多個串流而設計的。因此我們設計了一種針對串流360度方塊視頻的搶占式多路復用DASH串流的ABR算法。我們的演算法設計基於三個目標:(i)防止影片緩衝區無內容、(ii)避免影片間畫質的差異太大、以及(iii) 最大化平均影片畫質。 我們利用一些開源專案在Linux系統中實現我們提出的解決方案。我們的實驗結果顯示，與其他基準演算法相比，我們的算法:(i)當網路資源有限時，平均將緩衝計數減少3.2次，緩衝時間最多減少2.54秒， (ii)最多達到網路資源利用率提高40.02%， 以及(iii)在5-15 Mbps頻寬下提供39-49 dB的良好平均V-PSNR。

We design, implement, and evaluate a tiled DASH streaming system for 360° videos using QUIC/UDP protocol, in which multiplexed and prioritized streams are leveraged for sending urgent tiles that are about to miss their playout time. In particular, we develop a new architecture to concurrently request for regular tiled segments at lower priorities and urgent tiled segments at higher priorities as multiple streams over a single QUIC connection. Several core components, including the fixation prediction algorithm, fast tile selector, and Adaptive Bit Rate (ABR) algorithm are designed for this new architecture. Compared to streaming 2D planar videos, streaming 360° tiled videos using DASH to head-mounted displays is much more challenging. To the best of our knowledge, most existing DASH ABR algorithms are not designed for multiple and concurrent streams. Therefore, we design an ABR algorithm tailored for preemptive multiplexed DASH streams carrying 360° tiled videos. A suite of design decisions are made for three design objectives: (i) preventing buffer under-run, (ii) avoiding large quality jumps, and (iii) maximizing average quality. Capitalizing a few open-source projects, we implement our proposed solutions in a real end-to-end Linux system. Our experiment results show that compared to the baseline algorithms, our algorithm: (i) averagely reduces the rebuffering counts by up to 3.2 and rebuffering time by up to 2.54 s when the bandwidth is limited, (ii) achieves at most 40.02% higher bandwidth utilization, and (iii) delivers good average V-PSNR at 39–49 dB under 5–15 Mbps bandwidth.

Acknowledgments ---------- i
致謝 ---------- ii
Abstract ---------- iii
中文摘要 ---------- iv
1 Introduction ---------- 1
2 Background ---------- 6
3 Proposed Preemptive Multiplexed System ---------- 10
4 Preemptive Multiplexed Adaptive Bit Rate Algorithms ---------- 14
5 Implementations and Evaluations ---------- 23
6 Related Work ---------- 35
7 Conclusion and Future Work ---------- 38
Bibliography ---------- 40

[1] S. Arisu and A. C. Begen. Quickly starting media streams using QUIC. In Proc. of Packet Video Workshop (PV’18), pages 1–6, Amsterdam, Netherlands, June 2018.
[2] I. Bauermann, M. Mielke, and E. Steinbach. H.264 based coding. In Proc. of International Conference Computer Vision and Graphics (ICCVG’04), pages 209– 215, 2004.
[3] Bert Hubert. tc. https://linux.die.net/man/8/tc, 2018.
[4] D. Bhat, R. Deshmukh, and M. Zink. Improving QoE of ABR streaming sessions through QUIC retransmissions. In Proc. of ACM International Conference on Multimedia (MM’18), pages 1616–1624, Seoul, South Korea, October 2018.
[5] D. Bhat, A. Rizk, and M. Zink. Not so QUIC: A performance study of DASH over QUIC. In Proc. of ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV’17), pages 13–18, Taipei, Taiwan, June 2017.
[6] Daniel Stenberg. libcurl. https://curl.haxx.se/libcurl/, 2019.
[7] F. Dobrian, V. Sekar, A. Awan, I. Stoica, D. Joseph, A. Ganjam, and J. Zhan. Understanding the impact of video quality on user engagement. In Proc. of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM’11), pages 362–373, Toronto, Ontario, August 2011.
[8] 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 Proc. of ACM Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV’17), pages 67–72, Taipei, Taiwan, June 2017.
[9] FOVE Inc. Eye tracking VR DEVKIT. http://tinyurl.com/y6rgj5mz, 2019.
[10] C. Fu, L. Wan, T. Wong, and C. Leung. The rhombic dodecahedron map: An efficient scheme for encoding panoramic video. IEEE Transactions on Multimedia, 11(4):634–644, 2009.
[11] Google Inc. The chromium projects. https://www.chromium.org/quic/playing-with- quic, 2019.
[12] B. Hayes, Y. Chang, and G. Riley. Omnidirectional adaptive bitrate media delivery using MPTCP/QUIC over an SDN architecture. In Proc. of IEEE Global Communications Conference (GLOBECOM’17), pages 1–6, Singapore, Singapore, December 2017.
[13] B. Hayes, Y. Chang, and G. Riley. Controlled unfair adaptive 360 VR video delivery over an MPTCP/QUIC architecture. In Proc. of IEEE International Conference on Communications (ICC’18), pages 1–6, Kansas City, MO, May 2018.
[14] B.Hayes, Y.Chang, and G.Riley. Scaling 360-degree adaptive bitrate videod elivery over an SDN architecture. In Proc. of International Conference on Computing, Net- working and Communications (ICNC’18), pages 604–608, Maui, HI, March 2018.
[15] M. Hosseini and V. Swaminathan. Adaptive 360 VR video streaming: Divide and conquer. In Proc. of IEEE International Symposium on Multimedia (ISM’16), pages 107–110, San Jose, CA, December 2016.
[16] C.-F. Hsu, A. Chen, C.-H. Hsu, C.-Y. Huang, C.-L. Lei, and K.-T. Chen. Is foveated rendering perceivable in virtual reality?: Exploring the efficiency and consistency of quality assessment methods. In Proc. of ACM International Conference on Multimedia (MM’17), pages 55–63, Mountain View, California, October 2017.
[17] 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. In Proc. of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM’14), pages 187–198, Chicago, Illinois, August 2014.
[18] 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, February 2014.
[19] A. R. Jime ́nez, F. Seco, C. Prieto, and J. I. Guevara. A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. In Proc. of IEEE International Symposium on Intelligent Signal Processing (WISP’09), pages 37–42, Budapest, Hungary, August 2009.
[20] N. Kan, J. Zou, K. Tang, C. Li, N. Liu, and H. Xiong. Deep reinforcement learning-based rate adaptation for adaptive 360-degree video streaming. In Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19), pages 4030–4034, Brighton, United Kingdom, May 2019.
[21] H. Kimata, S. Shimizu, Y. Kunita, M. Isogai, and Y. Ohtani. Panorama video coding for user-driven interactive video application. In Proc. of IEEE International Symposium on Consumer Electronics (ISCE’09), pages 112–114, 2009.
[22] J. Kua, G. Armitage, and P. Branch. A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Communications Surveys Tutorials, 19(3):1842–1866, March 2017.
[23] A.Langley, A.Riddoch, A.Wilk, A.Vicente, C.Krasic, D.Zhang, F.Y.F.Kouranov, I. Swett, J. Iyengar, J. Bailey, J. Dorfman, J. Roskind, J. Kulik, P. Westin, R. Tenneti, R. Shade, R. Hamilton, V. Vasiliev, W.-T. Chang, and Z. Shi. The QUIC transport protocol: Design and internet-scale deployment. In Proc. of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM’17), pages 183– 196, Los Angeles, CA, August 2017.
[24] 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, April 2014.
[25] Linux. Shared memory. shorturl.at/kNSW8, 2019.
[26] W.-C. Lo, C.-L. Fan, J. Lee, C.-Y. Huang, K.-T. Chen, and C.-H. Hsu. 360◦ video viewing dataset in head-mounted virtual reality. In Proc. of ACM International Conference on Multimedia Systems (MMSys’17), pages 211–216, Taipei, Taiwan, June 2017.
[27] W.-C. Lo, C.-L. Fan, S.-C. Yen, and C.-H. Hsu. Performance measurements of 360◦ video streaming to head-mounted displays over live 4g cellular networks. In Proc. of Asia-Pacific Network Operations and Management Symposium(APNOMS’17), pages 205–210, Seoul, South Korea, September 2017.
[28] Luis MartinGarcia. Tcpdump. https://www.tcpdump.org/, 2018.
[29] A. Mavlankar and B. Girod. Video streaming with interactive pan/tilt/zoom. In
High-Quality Visual Experience, pages 431–455. Springer, 2010.
[30] K. Ng, S. Chan, and H. Shum. Data compression and transmission aspects of panoramic videos. IEEE Transactions on Circuits and Systems for Video Technology, 15(1):82–95, 2005.
[31] A. Nguyen, Z. Yan, and K. Nahrstedt. Your attention is unique: Detecting 360- degree video saliency in head-mounted display for head movement prediction. In Proc. of ACM International Conference on Multimedia (MM’18), pages 1190–1198, Seoul, Republic of Korea, October 2018.
[32] D. Nguyen, H. T. Tran, and T. C. Thang. A client-based adaptation framework for 360-degree video streaming. Journal of Visual Communication and Image Representation, 59:231–243, February 2019.
[33] D. H. Nguyen, M. Nguyen, N. P. Ngoc, and T. C. Thang. An adaptive method for low-delay 360 VR video streaming over HTTP/2. In Proc. of International Conference on Communications and Electronics (ICCE’18), pages 261–266, Hue, Vietnam, July 2018.
[34] D. V. Nguyen, H. T. T. Tran, A. T. Pham, and T. C. Thang. A new adaptation approach for viewport-adaptive 360-degree video streaming. In Proc. of IEEE International Symposium on Multimedia (ISM’17), pages 38–44, Taichung, Taiwan, December 2017.
[35] D. V. Nguyen, H. T. T. Tran, A. T. Pham, and T. C. Thang. An optimal tile-based approach for viewport-adaptive 360-degree video streaming. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 9(1):29–42, March 2019.
[36] M. Nguyen, D. H. Nguyen, C. T. Pham, N. P. Ngoc, D. V. Nguyen, and T. C. Thang. An adaptive streaming method of 360 videos over HTTP/2 protocol. In Proc. of NAFOSTED Conference on Information and Computer Science (NICS’18), pages 302–307, Hanoi, Vietnam, November 2017.
[37] L. Oculus VR. Facebook Oculus Rift. https://www.oculus.com/, 2019.
[38] M. Palmer, T. Kruger, B. Chandrasekaran, and A. Feldmann. The QUIC fix for optimal video streaming. In Proc. of the Workshop on the Evolution, Performance, and Interoperability of QUIC (EPIQ’18), pages 43–49, Heraklion, Greece, December 2018.
[39] Parikshit Juluri. AStream. https://github.com/pari685/AStream, 2018.
[40] S. Petrangeli, V. Swaminathan, M. Hosseini, and F. D. Turck. An HTTP/2-based adaptive streaming framework for 360◦ virtual reality videos. In Proc. of ACM International Conference on Multimedia (MM’17), pages 306–314, Mountain View, California, October 2017.
[41] Y. Sani, A. Mauthe, and C. Edwards. Adaptive bitrate selection: A survey. IEEE Communications Surveys Tutorials, 19(4):2985–3014, July 2017.
[42] M.Seufert, S.Egger, M.Slanina, T.Zinner, T.Hoßfeld, and P.Tran-Gia. A surveyon quality of experience of HTTP adaptive streaming. IEEE Communications Surveys Tutorials, 17(1):469–492, September 2015.
[43] T. Stockhammer. Dynamic adaptive streaming over HTTP –: standards and design principles. In Proc. of the ACM Conference on Multimedia Systems (MMSys’11), pages 133–144, San Jose, CA, February 2011.
[44] H. Strasburger, I. Rentschler, and M. Ju ̈ttner. Peripheral vision and pattern recognition: A review. Journal of Vision, 11(5):13–13, 12 2011.
[45] C. Timmerer and A. Bertoni. Advanced transport options for the dynamic adaptive streaming over HTTP. Technical report, Alpen-Adria-Universitat Klagenfurt, Institute of Information Technology (ITEC), Austria, 2016.
[46] Transparency Market Research Inc. Head mounted display
(helmet mounted display, wearable computing glasses) market. https://www.transparencymarketresearch.com/head-mounted-displays.html, 2015.
[47] C. Wang, A. Rizk, and M. Zink. Squad: A spectrum-based quality adaptation for dynamic adaptive streaming over HTTP. In Proc. of International Conference on Multimedia Systems (MMsys’16), pages 1–112, Klagenfurt, Austria, May 2016.
[48] 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 Proc. of IEEE Conference on Computer Communications (INFOCOM’18), pages 953–961, Honolulu, HI, April 2018.
[49] M. B. Yahia, Y. L. Louedec, G. Simon, and L. Nuaymi. HTTP/2-based streaming solutions for tiled omnidirectional videos. In Proc. of IEEE International Symposium on Multimedia (ISM’18), pages 89–96, Taichung, Taiwan, Taiwan, December 2018.
[50] S.-C. Yen, C.-L. Fan, and C.-H. Hsu. Streaming 360◦ videos to head-mounted virtual reality using DASH over QUIC transport protocol. In Proc. of ACM Workshop Packet Video Workshop (PV’19), pages 7–12, Amherst, MA, June 2019.
[51] M. Yu, H. Lakshman, and B. Girod. A framework to evaluate omnidirectional video coding schemes. In Prof. of IEEE International Symposium on Mixed and Augmented Reality (ISMAR’15), pages 31–36, Fukuoka, Japan, September 2015.
[52] A. Zare, A. Aminlou, M. M. Hannuksela, and M. Gabbouj. HEVC-compliant tile-based streaming of panoramic video for virtual reality applications. In Proc. of ACM International Conference on Multimedia (MM’16), pages 601–605, Amster- dam, The Netherlands, October 2016.
[53] Y. Zhang, P. Zhao, K. Bian, Y. Liu, L. Song, and X. Li. DRL360: 360-degree video streaming with deep reinforcement learning. In Proc. of IEEE Conference on Computer Communications (INFOCOM’19), pages 1252–1260, Paris, France, May 2019.