隨著視訊串流流量的快速成長，自適應視訊串流系統須在網路狀況持續波動下兼顧高視訊品質與播放連續性。Multipath QUIC（MP-QUIC）擴充了 QUIC，使其能在傳輸層聚合多條網路路徑的頻寬。不同於 Multipath TCP（MPTCP）可能受到連線層級隊頭阻塞（Head-of-Line Blocking）的影響，MP-QUIC 提供獨立的串流多工機制，使多個串流能夠並行傳輸且互不阻塞。可調式視訊編碼（Scalable Video Coding, SVC）將影片編碼為一個基礎層（BL）與多個增強層（ELs），其中 BL 提供可解碼的最低品質，而每個 EL 則逐步提升視訊品質。SVC的分層結構能與 MP-QUIC的串流多工能力相互配合，使每個 BL 與 EL 能夠對應至獨立的 QUIC 串流並透過多條路徑傳輸。然而，現有結合 SVC與多路徑傳輸的方法，仍缺乏能夠在動態網路環境下同時協調視訊品質選擇、播放緩衝區安全與進行中 EL 終止的完整機制。為了解決上述問題，本研究提出基於 MP-QUIC 的分段式階段感知 SVC自適應視訊串流（Segment-based and Stage-aware SVC's Adaptive Video Streaming, SS-SAVS）系統。SS-SAVS 具有三項主要特徵：(i) 將每個視訊分段中的每個 SVC 層對應至獨立的 QUIC 串流，並將串流過程劃分為五個由緩衝區門檻控制的階段，再透過品質增強分段集合（Quality Enhancement Segment Set, QESS）機制逐步提升連續分段的視訊品質；(ii) 採用最早完成時間優先（Earliest Completion-time First, ECF）排程器，並根據預期完成時間將封包分配至不同網路路徑；以及 (iii) 當播放連續性面臨風險時，使用 RESET_STREAM frame 終止進行中的 EL 下載，使系統能夠更早開始必要的 BL 補充。本研究於具有隨機背景流量的三條異質網路路徑環境下進行效能評估，並將 SS-SAVS 與 SS-SAVS-NCT、ASMQ、MP-SVC 及 BLS-ESC 進行比較。效能評估結果顯示，SS-SAVS 達成所有比較方法中最低的總播放停頓時間 0.600 秒。相較於 SS-SAVS-NCT，SS-SAVS 的總播放停頓時間與平均播放停頓時間分別降低 33.3% 及 33.3%，而其平均品質等級、平均視訊位元率與平均吞吐量皆與 SS-SAVS-NCT 相差低於 0.5%。相較於 ASMQ、MP-SVC 及 BLS-ESC，SS-SAVS 的總播放停頓時間分別降低 97.4%、97.9% 及 91.7%；平均品質等級分別提升 36.5%、50.7% 及 35.3%；平均視訊位元率則分別提升 71.0%、68.4% 及 65.7%。此外，SS-SAVS 達成最高的平均吞吐量 17.98 Mbps，相較於 ASMQ、MP-SVC 及 BLS-ESC 分別提升 80.7%、79.8% 及65.7%。SS-SAVS 亦達成最低的平均品質切換幅度 1.056，相較於 ASMQ、MP-SVC 及 BLS-ESC 分別降低 29.6%、33.9% 及 13.8%。雖然漸進式 QESS 機制使 SS-SAVS 的品質切換頻率高於 ASMQ 與 BLS-ESC，但大多數品質變化皆維持在接近單一品質層級的範圍內。因此，SS-SAVS 能夠在嚴重且動態的多路徑頻寬變化下，同時提供良好的播放連續性、高視訊品質、高頻寬利用率與漸進式品質變化。

With the rapid growth of video streaming traffic, adaptive video streaming systems must deliver high video quality and maintain playback continuity under fluctuating network conditions. Multipath QUIC (MP-QUIC) extends QUIC and allows the transport layer to aggregate the bandwidth of multiple network paths. Unlike Multipath TCP (MPTCP), which can suffer from connection-level head-of-line blocking, MP-QUIC provides independent stream multiplexing that allows multiple streams to be transmitted concurrently without blocking each other. Scalable Video Coding (SVC) encodes a video into a base layer (BL) and multiple enhancement layers (ELs). The BL provides the minimum decodable quality, and each successive EL improves the video quality. The layered structure of SVC can align with the stream multiplexing capability of MP-QUIC because each BL and EL can be mapped to an independent QUIC stream for transmission across multiple paths. However, existing approaches that combine SVC with multipath transmission still lack a complete mechanism that coordinates video quality selection, playback-buffer safety, and in-progress EL termination under dynamic network conditions. To address these problems, this work proposes the Segment-based and Stage-aware SVC's Adaptive Video Streaming (SS-SAVS) system using the Multi-Path QUIC Networking Protocol. SS-SAVS has three key features: (i) each SVC layer of each video segment is mapped to an independent QUIC stream, and the streaming session is divided into five buffer-threshold-governed stages with a Quality Enhancement Segment Set (QESS) mechanism for progressive quality enhancement; (ii) the Earliest Completion-time First (ECF) scheduler assigns packets to different paths according to their expected completion times; and (iii) the RESET_STREAM frame terminates an in-progress EL when playback continuity is at risk, which allows the system to start the required BL replenishment earlier. The system is evaluated over three heterogeneous paths with stochastic background traffic and is compared with SS-SAVS-NCT, ASMQ, MP-SVC, and BLS-ESC. The performance evaluation results show that SS-SAVS achieves the lowest total playback suspension duration of 0.600 seconds. Compared with SS-SAVS-NCT, SS-SAVS reduces the total and average playback suspension durations by 33.3% and 33.3%, respectively, while its average quality level, average video bitrate, and average throughput remain within 0.5% of those of SS-SAVS-NCT. Compared with ASMQ, MP-SVC, and BLS-ESC, SS-SAVS reduces the total playback suspension duration by 97.4%, 97.9%, and 91.7%, respectively; increases the average quality level by 36.5%, 50.7%, and 35.3%, respectively; increases the average video bitrate by 71.0%, 68.4%, and 65.7%, respectively; and increases the average throughput by 80.7%, 79.8%, and 65.7%, respectively. SS-SAVS also reduces the average quality switch magnitude by 29.6%, 33.9%, and 13.8% compared with ASMQ, MP-SVC, and BLS-ESC, respectively. Although the progressive QESS mechanism produces more frequent quality changes than ASMQ and BLS-ESC, most changes remain close to one quality level. Therefore, SS-SAVS achieves high video quality and strong playback continuity with gradual quality transitions.

[1] Ericsson, "Ericsson Mobility Report," Jun. 2025. [Online]. Available: https://www.ericsson.com/en/reports-and-papers/mobility-report/reports.
[2] International Telecommunication Union, Measuring Digital Development: Facts and Figures 2025. Geneva, Switzerland: ITU, 2025. [Online]. Available: https://www.itu.int/en/ITU-D/Statistics/pages/facts/default.aspx
[3] I. Sodagar, "The MPEG-DASH Standard for Multimedia Streaming over the Internet," IEEE MultiMedia, vol. 18, no. 4, pp. 62-67, Apr. 2011.
[4] 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, vol. 22, no. 1, pp. 326-340, Feb. 2014.
[5] 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, vol. 44, no. 4, pp. 187-198, Oct. 2014.
[6] K. Spiteri, R. Urgaonkar, and R. K. Sitaraman, "BOLA: Near-Optimal Bitrate Adaptation for Online Videos," IEEE/ACM Transactions on Networking, vol. 28, no. 4, pp. 1698-1711, Aug. 2020.
[7] H. Mao, R. Netravali, and M. Alizadeh, “Neural Adaptive Video Streaming with Pensieve,” in Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM 2017), Los Angeles, CA, USA, 2017, pp. 197-210.
[8] M. Aguayo, L. Bellido, C. M. Lentisco, and E. Pastor, "DASH Adaptation Algorithm Based on Adaptive Forgetting Factor Estimation," IEEE Transactions on Multimedia, vol. 20, no. 5, pp. 1224-1232, May 2018.
[9] Z. Akhtar, Y. S. Nam, R. Govindan, S. Rao, J. Chen, E. Katz-Bassett, B. Ribeiro, J. Zhan, and H. Zhang, “Oboe: Auto-Tuning Video ABR Algorithms to Network Conditions,” in Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM 2018), Budapest, Hungary, 2018, pp. 44-58.
[10] M. Naresh, P. Saxena, and M. Gupta, "PPO-ABR: Proximal Policy Optimization Based Deep Reinforcement Learning for Adaptive BitRate Streaming," in Proceedings of the IEEE International Wireless Communications and Mobile Computing Conference (IWCMC 2023), Marrakesh, Morocco, 2023, pp. 199-204.
[11] T. Chen, Y. Lin, N. Christianson, Z. Akhtar, S. Dharmaji, M. Hajiesmaili, A. Wierman, and R. K. Sitaraman, "SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming," in Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM 2024), Sydney, NSW, Australia, 2024, pp. 613-644.
[12] Z. Zhong, M. Liu, L. Yang, Y. Wang, Y. Xu, and J.-N. Hwang, “Video Streaming with Kairos: An MPC-Based ABR with Streaming-Aware Throughput Prediction,” in Proceedings of the 35th Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV 2025), New York, NY, USA, 2025, pp. 84-90.
[13] S. Yau, S. Awiphan, J. Bootkrajang, and J. Katto, "A Robust Throughput Estimation in Edge-Assisted Adaptive Bitrate Streaming Networks," IEEE Access, vol. 13, pp. 152598-152607, 2025.
[14] C. Timmerer, H. Amirpour, F. Tashtarian, S. Afzal, A. Rizk, M. Zink, and H. Hellwagner, "HTTP Adaptive Streaming: A Review on Current Advances and Future Challenges," ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 21, no. 7, Art. no. 198, Jul. 2025.
[15] M. Alsader, A. A. Barakabitze, and I.-H. Mkwawa, "QoE-Driven Adaptive Video Streaming: Architectures, Techniques, and Future Research Challenges toward 6G Networks," IEEE Access, vol. 13, pp. 157408-157441, 2025.
[16] H. Schwarz, D. Marpe, and T. Wiegand, "Overview of the Scalable Video Coding Extension of the H.264/AVC Standard," IEEE Transactions on Circuits and Systems for Video Technology, vol. 17, no. 9, pp. 1103-1120, Sep. 2007.
[17] S. Mori and M. Bandai, "QoE-Aware Quality Selection Method for Adaptive Video Streaming with Scalable Video Coding," in Proceedings of the IEEE International Conference on Consumer Electronics (ICCE 2018), Las Vegas, NV, USA, 2018, pp. 1-4.
[18] A. Elgabli, V. Aggarwal, S. Hao, F. Qian, and S. Sen, "LBP: Robust Rate Adaptation Algorithm for SVC Video Streaming," IEEE/ACM Transactions on Networking, vol. 26, no. 4, pp. 1633-1645, Aug. 2018.
[19] Y. Liu, B. Jiang, T. Guo, R. K. Sitaraman, D. Towsley, and X. Wang, “Grad: Learning for Overhead-Aware Adaptive Video Streaming with Scalable Video Coding,” in Proceedings of the 28th ACM International Conference on Multimedia (MM 2020), New York, NY, USA, 2020, pp. 349-357.
[20] C. M. Huang and H. I. Wang, “Fuzzy-Rule-Decided Small Cell Offloading for Rate-Adaptive SVC-DASH Video Streaming over the Vehicle Environment,” Computer Communications, vol. 212, pp. 1-20, 2023.
[21] B. Han, F. Qian, L. Ji, and V. Gopalakrishnan, “MP-DASH: Adaptive Video Streaming over Preference-Aware Multipath,” in Proceedings of the 12th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2016), New York, NY, USA, 2016, pp. 129-143.
[22] J. Zhao, J. Liu, C. Zhang, Y. Cui, Y. Jiang, and W. Gong, "MPTCP+: Enhancing Adaptive HTTP Video Streaming over Multipath," in Proceedings of the 28th IEEE/ACM International Symposium on Quality of Service (IWQoS 2020), Hangzhou, China, 2020, pp. 1-6.
[23] A. Elgabli, K. Liu, and V. Aggarwal, "Optimized Preference-Aware Multi-Path Video Streaming with Scalable Video Coding," IEEE Transactions on Mobile Computing, vol. 19, no. 1, pp. 159-172, Jan. 2020.
[24] S. G. Ozcan and M. Sayit, “Multipath Transmission Aware ABR Algorithm for SVC HAS,” Computer Communications, vol. 201, pp. 20-36, Mar. 2023.
[25] J. Cai, F. Chen, D. Zhu, and P. Chen, "SVC-Based and Flow Assignment Strategy for Multi-Path Concurrent Video Transmission with Low-Latency," in Proceedings of the 3rd Asia Conference on Computers and Communications (ACCC 2022), Shanghai, China, 2022, pp. 109-115.
[26] A. Gohar and S. Lee, “Multipath Dynamic Adaptive Streaming over HTTP Using Scalable Video Coding in Software Defined Networking,” Applied Sciences, vol. 10, no. 21, Art. no. 7691, 2020.
[27] J. Iyengar and M. Thomson, “QUIC: A UDP-Based Multiplexed and Secure Transport,” RFC 9000, May 2021. [Online]. Available: https://datatracker.ietf.org/doc/rfc9000/.
[28] A. Langley, A. Riddoch, A. Wilk, A. Vicente, C. Krasic, D. Zhang, F. Yang, 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 Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM 2017), Los Angeles, CA, USA, 2017, pp. 183-196.
[29] J. S. Sidhu and A. Bentaleb, “Video Streaming over QUIC: A Comprehensive Study,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 22, no. 4, Art. no. 94, Apr. 2026.
[30] Q. De Coninck and O. Bonaventure, “Multipath QUIC: Design and Evaluation,” in Proceedings of the 13th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2017), New York, NY, USA, 2017, pp. 160-166.
[31] Y. Liu, Y. Ma, Q. De Coninck, O. Bonaventure, C. Huitema, and M. Kühlewind, “Managing Multiple Paths for a QUIC Connection,” Internet Eng. Task Force, Internet-Draft draft-ietf-quic-multipath-20, Feb. 2026, Work in Progress. [Online]. Available: https://datatracker.ietf.org/doc/draft-ietf-quic-multipath/20/.
[32] W. Yang, J. Cao, and F. Wu, "Adaptive Video Streaming with Scalable Video Coding Using Multipath QUIC," in Proceedings of the IEEE International Performance, Computing, and Communications Conference (IPCCC 2021), Austin, TX, USA, 2021, pp. 1-7.
[33] Y. S. Lim, E. M. Nahum, D. Towsley, and R. J. Gibbens, “ECF: An MPTCP Path Scheduler to Manage Heterogeneous Paths,” in Proceedings of the 13th ACM International Conference on Emerging Networking Experiments and Technologies (CoNEXT 2017), New York, NY, USA, 2017, pp. 147-159.
[34] M. Nguyen, H. Amirpour, C. Timmerer, and H. Hellwagner, “Scalable High Efficiency Video Coding Based HTTP Adaptive Streaming over QUIC,” in Proceedings of the Workshop on the Evolution, Performance, and Interoperability of QUIC (EPIQ 2020), 2020, pp. 28-34.
[35] C. Cui, Y. Lu, S. Li, J. Li, and Z. Ruan, "DASH+: Download Multiple Video Segments with Stream Multiplexing of QUIC," in Proceedings of the 10th International Conference on Advanced Cloud and Big Data (CBD 2022), Guilin, China, 2022, pp. 66-72.
[36] M. Nguyen, D. Lorenzi, F. Tashtarian, H. Hellwagner, and C. Timmerer, "DoFP+: An HTTP/3-Based Adaptive Bitrate Approach Using Retransmission Techniques," IEEE Access, vol. 10, pp. 109565-109579, Oct. 2022.
[37] S. Chellappa, R. Farahani, R. Bartos, and H. Hellwagner, “Context-Aware HTTP Adaptive Video Streaming Utilizing QUIC's Stream Priority,” in Proceedings of the 2nd Mile-High Video Conference (MHV 2023), 2023, pp. 144-145.
[38] M. H. Dwijaksara, M. H. Hilman, C. Lee, and W. Lee, “Rate Adaptation Technique for Media Streaming over QUIC with Limited Backhaul,” IEEE Access, vol. 12, pp. 139028-139041, Sep. 2024.
[39] J. S. Sidhu and A. Bentaleb, “CAQ: Connection-Aware Adaptive QUIC Configurations for Enhanced Video Streaming,” in Proceedings of the 35th Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV 2025), New York, NY, USA, 2025, pp. 8-14.
[40] G. Tremols Suarez, J. S. Sidhu, and A. Bentaleb, “PulseQUIC: Enhancing QUIC-Based Video Streaming through DRL-Guided Adaptive Pacing,” in Proceedings of the 3rd Workshop on Emerging Multimedia Systems (EMS 2025), New York, NY, USA, 2025, pp. 43-48.
[41] H. Zeng, L. Cui, F. P. Tso, and Z. Zhang, “Optimizing Multipath QUIC Transmission over Heterogeneous Paths,” Computer Networks, vol. 215, Art. no. 109198, Oct. 2022.
[42] X. Liang, B. Zhao, W. Peng, and T. Wang, "Towards Effective Multipath Scheduling with Multipath QUIC in Heterogeneous Paths," in Proceedings of the 10th International Conference on Information Systems and Computing Technology (ISCTech 2022), Guilin, China, 2022, pp. 472-479.
[43] Y. Xing, K. Xue, Y. Zhang, J. Han, J. Li, D. S. L. Wei, R. Li, Q. Sun, and J. Lu, "A Stream-Aware MPQUIC Scheduler for HTTP Traffic in Mobile Networks," IEEE Transactions on Wireless Communications, vol. 22, no. 4, pp. 2775-2788, Apr. 2023.
[44] X. Han, B. Han, J. Li, and C. Song, “Multi-Agent DRL-Based Multipath Scheduling for Video Streaming with QUIC,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 20, no. 7, Art. no. 211, Jul. 2024.
[45] Y. Ren, C. Wu, A. Shan, J. Chen, Y. Lin, Z. Du, L. Zhong, and S. Djahel, "A QoS-Driven Fuzzy Logic-Based Adaptive MPQUIC Scheduler for Heterogeneous Networks," IEEE Transactions on Network Science and Engineering, vol. 13, pp. 2586-2603, 2026.
[46] Z. Zheng, Y. Ma, Y. Liu, F. Yang, Z. Li, Y. Zhang, J. Zhang, W. Shi, W. Chen, D. Li, Q. An, H. Hong, H. H. Liu, and M. Zhang, “XLINK: QoE-Driven Multi-Path QUIC Transport in Large-Scale Video Services,” in Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM 2021), New York, NY, USA, 2021, pp. 418-432.
[47] B. Kimura, S. Ferlin, T. Paiva, T. Mahmoodi, A. Brunstrom, and O. Alay, “Evaluating Adaptive Video Streaming over Multipath QUIC with Shared Bottleneck Detection,” ACM Transactions on Multimedia Computing, Communications, and Applications, vol. 21, no. 9, Art. no. 246, Sep. 2025.
[48] G. Lv, Q. Wu, Y. Liu, Z. Li, Q. Tan, F. Yang, W. Chen, Y. Ma, H. Guo, Y. Chen, and G. Xie, “Chorus: Coordinating Mobile Multipath Scheduling and Adaptive Video Streaming,” in Proceedings of the 30th Annual International Conference on Mobile Computing and Networking (MobiCom 2024), New York, NY, USA, 2024, pp. 246-262.
[49] W. Yang, L. Cai, S. Shu, A. Sepahi, Z. Huang, and J. Pan, “QoS-Driven Contextual MAB for MPQUIC Supporting Video Streaming in Mobile Networks,” IEEE Transactions on Mobile Computing, vol. 24, no. 4, pp. 3274-3287, Apr. 2025.
[50] H. Kang, S. Hong, D. Kang, and D. An, "Deadline-Aware Redundant Transmission on Multi-Paths for Real-Time Service," IEEE Access, vol. 13, pp. 51589-51602, Mar. 2025.
[51] J. Han, J. Liu, K. Xue, Q. Sun, and J. Lu, "Toward High-Quality Real-Time Video Streaming: An Efficient Multi-Stream and Multi-Path Scheduling Framework," IEEE Transactions on Networking, vol. 33, no. 4, pp. 1826-1839, Aug. 2025.
[52] Z. Wei, Q. Li, T. Zhao, C. Luo, C. Ouyang, X. Yu, D. Zhao, and Y. Jiang, “EMVOD: Elastic Multi-Path QUIC Scheduling for CDN Video-on-Demand Service,” in Proceedings of the 21st European Conference on Computer Systems (EuroSys 2026), New York, NY, USA, 2026, pp. 532-547.
[53] B. Lantz, B. Heller, and N. McKeown, “A Network in a Laptop: Rapid Prototyping for Software-Defined Networks,” in Proceedings of the 9th ACM SIGCOMM Workshop on Hot Topics in Networks (Hotnets-IX), New York, NY, USA, 2010, Art. no. 19, pp. 1-6.
[54] S. Ferlin, S. Kucera, H. Claussen, and Ö. Alay, "MPTCP Meets FEC: Supporting Latency-Sensitive Applications over Heterogeneous Networks," IEEE/ACM Transactions on Networking, vol. 26, no. 5, pp. 2005-2018, Oct. 2018.