本論文提出了基於移動邊緣計算（MEC）協助MPEG-DASH來進行HTTP自適應影音串流的服務，及考慮穩定度的畫質調整和MEC伺服器換手的控制方案。在所提出的方法中，MEC伺服器在無線行動網絡中扮演計算和緩存機制的角色。為了使MEC伺服器計算出較合適的畫質決策，本論文提出的方法包含（i）使用自適應濾波器估計使用者裝置的頻寬，（ii）透過估計的頻寬和APP回報的緩衝區情況，且（iii）考慮影片畫質的穩定性，從而選擇較合適的畫質。本論文的方法不僅考慮（i）使用者頻寬和緩衝區問題，還考慮（ii）自適應串流的長期和短期畫質變化。假定在一個MEC服務器與一個BS相聯的系統中，若使用者移動的話，可能會有換手連接的4G / 5G基站（BS），這導致MEC伺服器也會切換。因此，本論文提出BS / MEC伺服器換手的控制方案，可以使MEC伺服器切換時的畫質更加流暢。本論文提出的方法在4G LTE網絡環境中實現，使用Linux系統和USRP設備（實驗室規模的eNB系統）構建。根據實驗結果，提出的方法能提升對於無線行動網絡中的MPEG-DASH串流的影片畫質穩定度，包含在MEC服務器換手處理期間。

This thesis proposed the Mobile Edge Computing (MEC)-based video streaming with quality-aware video bitrate adaption and MEC server handoff control schemes using the MPEG-DASH video streaming architecture. Using the proposed method, the MEC sever plays the role of both computing and caching mechanisms in the remote video server-edge server-client 3-tier video streaming platform over the wireless mobile network. In this work, the proposed quality-aware video bitrate adaption and MEC server handoff control schemes are used to assist the MPEG-DASH video streaming. To calculate the video bit rate for each video segment of the MPEG-DASH video streaming, the proposed method (i) has the estimated bandwidth using the adaptive filter mechanism, (ii) derives some candidate video bit rates by considering the estimated bandwidth and the buffer occupancy situation in the client side, which was reported from the User APP, and then (iii) selects a video bit rate from the candidate ones considering video quality’s stability. For the video quality’s stability concern, the proposed method considered not only (i) both bandwidth and buffer issues but also (ii) the long-term quality variation and the short-term quality variation to have the adaptive video streaming. Since the user is moving, the attached 4G/5G Base Station (BS) can be changed, i.e., the BS handoff can happen, which results in the MEC server handoff, for which it is assumed that one MEC server is associated with one BS in this work. Thus, this work proposed the BS/MEC server handoff control scheme to make the playing quality smoother when the MEC server handoff happens. The proposed method has been implemented in the experimental 4G LTE network, which was built using the Linux system and the USRP device, which is a lab-scaled eNB system. The results of the performance evaluation shown that the proposed method has the more stable video quality, including during the MEC server handoff processing period, for the MPEG-DASH video streaming over the wireless mobile network.

[1] J. Guo, X. Gong, J. Liang, W. Wang, and X. Que, "An Optimized Hybrid Unicast/Multicast Adaptive Video Streaming Scheme Over MBMS-Enabled Wireless Networks," IEEE Transactions on Broadcasting, VOL. 64, no. 4, pp. 791-802, 2018.
[2] C. Xu, P. Zhang, S. Jia, M. Wang, and G.-M. Muntean, "Video streaming in content-centric mobile networks: Challenges and solutions," IEEE Wireless Communications, vol. 24, no. 5, pp. 157-165, 2017.
[3] A. A. Barakabitze et al., "QoE Management of Multimedia Streaming Services in Future Networks: a tutorial and survey," IEEE Communications Surveys & Tutorials, 2019.
[4] T. Stockhammer, "Dynamic Adaptive Streaming over HTTP-- Standards and Design Principles," in Proceedings of the 2nd annual ACM conference on Multimedia systems, pp. 133-144, 2011.
[5] I. Sodagar, "The MPEG-DASH Standard for Multimedia Streaming Over the Internet," IEEE MultiMedia, VOL. 18, no. 4, pp. 62-67, 2011.
[6] S. Zhou, P. P. Netalkar, Y. Chang, Y. Xu, and J. Chao, "The MEC-Based Architecture Design for Low-Latency and Fast Hand-Off Vehicular Networking," in Proceedings of the 88th IEEE Vehicular Technology Conference (VTC-Fall), pp. 1-7, 2018.
[7] N. Vineeth and H. Guruprasad, "A Survey on the Techniques Enhancing Video Streaming in VANETs," Int. J. Comput. Networking Wireless Mobile Commun, VOL. 3, pp. 37-46, 2013.
[8] T. Taleb, K. Samdanis, B. Mada, H. Flinck, S. Dutta, and D. Sabella, "On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration," IEEE Communications Surveys & Tutorials, VOL. 19, no. 3, pp. 1657-1681, 2017.
[9] H. Tanaka, M. Yoshida, K. Mori, and N. Takahashi, "Multi-access Edge Computing: A Survey," Journal of Information Processing, VOL. 26, pp. 87-97, 2018.
[10] M. Mehrabi, D. You, V. Latzko, H. Salah, M. Reisslein, and F. H. P. Fitzek, "Device-Enhanced MEC: Multi-Access Edge Computing (MEC) Aided by End Device Computation and Caching: A Survey," IEEE Access, VOL. 7, pp. 166079-166108, 2019.
[11] A. Mehrabi, M. Siekkinen, and A. Ylä-Jääski, "Edge Computing Assisted Adaptive Mobile Video Streaming," IEEE Transactions on Mobile Computing, VOL. 18, no. 4, pp. 787-800, 2018.
[12] D. Wang et al., "Adaptive Wireless Video Streaming Based on Edge Computing: Opportunities and Approaches," IEEE Transactions on Services Computing, vol. 12, no. 5, pp. 685-697, 2019.
[13] D. C. Montgomery, Introduction to Statistical Quality Control. John Wiley & Sons, 2007.
[14] Y. LeCun, Y. Bengio, and G. Hinton, "Deep Learning," nature, VOL. 521, no. 7553, pp. 436-444, 2015.
[15] G. A. Seber and A. J. Lee, Linear Regression Analysis. John Wiley & Sons, 2012.
[16] S. Haykin, Adaptive Filter Theory (3rd ed.). Prentice-Hall, Inc., 1996.
[17] Y. Li, P. A. Frangoudis, Y. Hadjadj-Aoul, and P. Bertin, "A Mobile Edge Computing-based Architecture for Improved Adaptive HTTP Video Delivery," in Proceedings of the 7th IEEE Conference on Standards for Communications and Networking (CSCN), pp. 1-6, 2016.
[18] 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 Proceedings of the 7th ACM Conference on Special Interest Group on Data Communication (SIGCOMM), pp. 187-198, 2014.
[19] C. Lai, R. Hwang, H. Chao, M. M. Hassan, and A. Alamri, "A Buffer-aware HTTP Live Streaming Approach for SDN-enabled 5G Wireless Networks," IEEE Network, VOL. 29, no. 1, pp. 49-55, 2015.
[20] Y. Guo, F. R. Yu, J. An, K. Yang, Y. He, and V. C. Leung, "Buffer-aware Streaming in Small-scale Wireless Networks: A Deep Reinforcement Learning Approach," IEEE Transactions on Vehicular Technology, VOL. 68, no. 7, pp. 6891-6902, 2019.
[21] Z. Jiang et al., "A Hierarchical Buffer Management Approach to Rate Adaptation for 360-Degree Video Streaming," IEEE Transactions on Vehicular Technology, 2019.
[22] C. Liu, I. Bouazizi, and M. Gabbouj, "Rate Adaptation for Adaptive HTTP Streaming," in Proceedings of the 2nd ACM annual Conference on Multimedia Systems, pp. 169-174, 2011.
[23] C. Zhou, C.-W. Lin, and Z. Guo, "mDASH: A Markov Decision-based Rate Adaptation Approach for Dynamic HTTP Streaming," IEEE Transactions on Multimedia, VOL. 18, no. 4, pp. 738-751, 2016.
[24] A. Bentaleb, A. C. Begen, and R. Zimmermann, "QoE-aware Bandwidth Broker for HTTP Adaptive Streaming Flows in an SDN-enabled HFC Network," IEEE Transactions on Broadcasting, VOL. 64, no. 2, pp. 575-589, 2018.
[25] C. James, M. Wang, and E. Halepovic, "BETA: Bandwidth-efficient Temporal Adaptation for Video Streaming over Reliable Transports," in Proceedings of the 10th ACM Multimedia Systems Conference, 2019, pp. 98-109.
[26] R. Immich, L. Villas, L. Bittencourt, and E. Madeira, "Multi-tier Edge-to-Cloud Architecture for Adaptive Video Delivery," in Proceedings of the 7th International Conference on Future Internet of Things and Cloud (FiCloud), pp. 23-30, 2019.
[27] S. Yang, Y. Tseng, C. Huang, and W. Lin, "Multi-Access Edge Computing Enhanced Video Streaming: Proof-of-Concept Implementation and Prediction/QoE Models," IEEE Transactions on Vehicular Technology, VOL. 68, no. 2, pp. 1888-1902, 2019.
[28] S. Han, H. Ma, D. Chen, Y. Wang, Y. Wu, and P. Zhang, "Streaming Video Optimization in Mobile Communications," in Proceedings of the 7th IEEE/CIC International Conference on Communications in China (ICCC), pp. 495-499, 2018.
[29] Z. Chang, X. Zhou, Z. Wang, H. Li, and X. Zhang, "Edge-assisted Adaptive Video Streaming with Deep Learning in Mobile Edge Networks," in Proceedings of the 9th IEEE Wireless Communications and Networking Conference (WCNC), pp. 1-6, 2019.
[30] E. Kurdoglu, Y. Liu, Y. Wang, Y. Shi, C. Gu, and J. Lyu, "Real-time Bandwidth Prediction and Rate Adaptation for Video Calls over Cellular Networks," in Proceedings of the 7th International Conference on Multimedia Systems, Klagenfurt, Austria, 2016.
[31] A. Bentaleb, C. Timmerer, A. C. Begen, and R. Zimmermann, "Bandwidth Prediction in Low-latency Chunked Streaming," in Proceedings of the 29th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video, Amherst, Massachusetts, 2019.
[32] C.-F. Lai, Y.-C. Chang, H.-C. Chao, M. S. Hossain, and A. Ghoneim, "A Buffer-aware QoS Streaming Approach for SDN-enabled 5G Vehicular Networks," IEEE Communications Magazine, VOL. 55, no. 8, pp. 68-73, 2017.
[33] C. Vallati, E. Mingozzi, and C. Benedetto, "Efficient Handoff Based on Link Quality Prediction for Video Streaming in Urban Transport Systems," Wireless Communications and Mobile Computing, VOL. 16, no. 15, pp. 2298-2314, 2016.
[34] H.-Y. Kung, C.-H. Chen, M.-H. Lin, and T.-Y. Wu, "Design of Seamless Handoff Control Based on Vehicular Streaming Communications," Journal of Internet Technology, VOL. 20, no. 7, pp. 2083-2097, 2019.
[35] L. Ma, S. Yi, and Q. Li, "Efficient Service Handoff across Edge Servers Via Docker Container Migration," in Proceedings of the 2nd ACM/IEEE Symposium on Edge Computing, San Jose, California, 2017.
[36] C. Zhang and Z. Zheng, "Task Migration for Mobile Edge Computing Using Deep Reinforcement Learning," Future Generation Computer Systems, VOL. 96, pp. 111-118, 2019.
[37] R. Radhakrishnan and A. Nayak, "Cross Layer Design for Efficient Video Streaming over LTE Using Scalable Video Coding," in Proceedings of the 22th IEEE International Conference on Communications (ICC), pp. 6509-6513, 2012.
[38] C. Wang, Z. Lin, S. Yang, and P. Lin, "Mobile Edge Computing-enabled Channel-aware Video Streaming for 4G LTE," in Proceedings of the 13th International Wireless Communications and Mobile Computing Conference (IWCMC), pp. 564-569, 2017.
[39] H. A. M. Ramli, F. N. M. Isa, A. F. Ismail, M. K. Hasan, and W. Hashim, "Impact of outdated CQI Report on Adapted Well-known Packet Scheduling Algorithm When Streaming Video," in Proceedings of the 3th International Conference on Space Science and Communication (IconSpace), pp. 383-388, 2015.
[40] K. Spiteri, R. Urgaonkar, and R. K. Sitaraman, "BOLA: Near-Optimal Bitrate Adaptation for Online Videos," in Proceedings of the 35th Annual IEEE International Conference on Computer Communications(IFNOCOM), pp. 1-9, 2016.