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An End-to-End Pipeline Perspective on Video Streaming in Best-Effort Networks: A Survey and Tutorial (2403.05192v3)

Published 8 Mar 2024 in cs.NI and cs.MM

Abstract: Remaining a dominant force in Internet traffic, video streaming captivates end users, service providers, and researchers. This paper takes a pragmatic approach to reviewing recent advances in the field by focusing on the prevalent streaming paradigm that involves delivering long-form two-dimensional videos over the best-effort Internet with client-side adaptive bitrate (ABR) algorithms and assistance from content delivery networks (CDNs). To enhance accessibility, we supplement the survey with tutorial material. Unlike existing surveys that offer fragmented views, our work provides a holistic perspective on the entire end-to-end streaming pipeline, from video capture by a camera-equipped device to playback by the end user. Our novel perspective covers the ingestion, processing, and distribution stages of the pipeline and addresses key challenges such as video compression, upload, transcoding, ABR algorithms, CDN support, and quality of experience. We review over 200 papers and classify streaming designs by their problem-solving methodology, whether based on intuition (simple heuristics), theory (formal optimization), or machine learning (generalizable data patterns). The survey further refines these methodology-based categories and characterizes each design by additional traits such as compatible codecs and use of super resolution. We connect the reviewed research to real-world applications by discussing the practices of commercial streaming platforms. Finally, the survey highlights prominent current trends and outlines future directions in video streaming.

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References (203)
  1. Per-Title Encode Optimization. Technology Blog, Netflix, https://netflixtechblog.com/per-title-encode-optimization-7e99442b62a2.
  2. A Holistic Survey of Multipath Wireless Video Streaming. Journal of Network and Computer Applications 212, article 103581 (2023). https://doi.org/10.1016/j.jnca.2022.103581
  3. FastTTPS: Fast Approach for Video Transcoding Time Prediction and Scheduling for HTTP Adaptive Streaming Videos. Cluster Computing 24, 3 (2021), 1605–1621. https://doi.org/10.1007/s10586-020-03207-x
  4. Video Transcoding: An Overview of Various Techniques and Research Issues. IEEE Transactions on Multimedia 7, 5 (2005), 793–804. https://doi.org/10.1109/TMM.2005.854472
  5. An Experimental Evaluation of Rate-Adaptive Video Players over HTTP. Signal Processing: Image Communication 27, 4 (2012), 271–287. https://doi.org/10.1016/j.image.2011.10.003
  6. Oboe: Auto-Tuning Video ABR Algorithms to Network Conditions. In SIGCOMM 2018. 44–58. https://doi.org/10.1145/3230543.3230558
  7. FastTrack: Minimizing Stalls for CDN-Based Over-the-Top Video Streaming Systems. IEEE Transactions on Cloud Computing 9 (2021), 1453–1466. https://doi.org/10.1109/TCC.2019.2920979
  8. Big-Data-Driven and AI-Based Framework to Enable Personalization in Wireless Networks. IEEE Communications Magazine 58, 3 (2020), 18–24. https://doi.org/10.1109/MCOM.001.1900533
  9. Ethem Alpaydin. 2020. Introduction to Machine Learning. MIT Press.
  10. CBA: Contextual Quality Adaptation for Adaptive Bitrate Video Streaming. In INFOCOM 2019. 1000–1008. https://doi.org/10.1109/INFOCOM.2019.8737418
  11. Sa’di Altamimi and Shervin Shirmohammadi. 2020. QoE-Fair DASH Video Streaming Using Server-Side Reinforcement Learning. ACM Transactions on Multimedia Computing, Communications and Applications 16, article 68 (2020), 1–21. https://doi.org/10.1145/3397227
  12. Xavier Amatriain. 2013. Big & Personal: Data and Models behind Netflix Recommendations. In BigMine 2013. 1–6. https://doi.org/10.1145/2501221.2501222
  13. Sevket Arisu and Ali C. Begen. 2018. Quickly Starting Media Streams Using QUIC. In PV 2018. 1–6. https://doi.org/10.1145/3210424.3210426
  14. Consumer Technology Association. 2022. CTA-5006: Web Application Video Ecosystem –- Common Media Server Data. November 2022, https://cdn.cta.tech/cta/media/media/resources/standards/pdfs/cta-5006-final.pdf.
  15. Christos G. Bampis and Alan C. Bovik. 2018. Feature-Based Prediction of Streaming Video QoE: Distortions, Stalling and Memory. Signal Processing: Image Communication 68 (2018), 218–228. https://doi.org/10.1016/j.image.2018.05.017
  16. QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey. IEEE Communications Surveys and Tutorials 22, 1 (2019), 526–565. https://doi.org/10.1109/COMST.2019.2958784
  17. Nabajeet Barman and Maria G. Martini. 2019. QoE Modeling for HTTP Adaptive Video Streaming – A Survey and Open Challenges. IEEE Access 7 (2019), 30831–30859. https://doi.org/10.1109/ACCESS.2019.2901778
  18. ABMA+: Lightweight and Efficient Algorithm for HTTP Adaptive Streaming. In MMSys 2016. 1–11. https://doi.org/10.1145/2910017.2910596
  19. Richard Bellman. 1966. Dynamic Programming. Science 153, 3731 (1966), 34–37. https://doi.org/10.1126/science.153.3731.34
  20. SDNDASH: Improving QoE of HTTP Adaptive Streaming Using Software Defined Networking. In MM 2016. 1296–1305. https://doi.org/10.1145/2964284.2964332
  21. Common Media Client Data (CMCD): Initial Findings. In NOSSDAV 2021. https://doi.org/10.1145/3458306.3461444
  22. Meta Reinforcement Learning for Rate Adaptation. In INFOCOM 2023. 1–10. https://doi.org/10.1109/INFOCOM53939.2023.10228951
  23. A Survey on Bitrate Adaptation Schemes for Streaming Media over HTTP. IEEE Communications Surveys and Tutorials 21, 1 (2019), 562–585. https://doi.org/10.1109/COMST.2018.2862938
  24. AdaptSize: Orchestrating the Hot Object Memory Cache in a Content Delivery Network. In NSDI 2017. 483–498. https://www.usenix.org/conference/nsdi17/technical-sessions/presentation/berger
  25. Improving QoE of ABR Streaming Sessions through QUIC Retransmissions. In MM 2018. 1616–1624. https://doi.org/10.1145/3240508.3240664
  26. Not So QUIC: A Performance Study of DASH over QUIC. In NOSSDAV 2017. 13–18. https://doi.org/10.1145/3083165.3083175
  27. Collaborative Joint Caching and Transcoding in Mobile Edge Networks. Journal of Network and Computer Applications 136 (2019), 86–99. https://doi.org/10.1016/j.jnca.2019.02.004
  28. Kashif Bilal and Aiman Erbad. 2017. Edge Computing for Interactive Media and Video Streaming. In FMEC 2017. 68–73. https://doi.org/10.1109/FMEC.2017.7946410
  29. Bitmovin. 2020. Bitmovin Video Developer. Report https://bitmovin.com/video-developer-report#pdf.
  30. Benjamin Bross et al. 2021. Overview of the Versatile Video Coding (VVC) Standard and its Applications. IEEE Transactions on Circuits and Systems for Video Technology 31, 10 (2021), 3736–3764. https://doi.org/10.1109/TCSVT.2021.3101953
  31. Thiago Luiz Alves Bubolz et al. 2019. Quality and Energy-Aware HEVC Transrating Based on Machine Learning. IEEE Transactions on Circuits and Systems I: Regular Papers 66, 6 (2019), 2124–2136. https://doi.org/10.1109/TCSI.2019.2903978
  32. End-to-End Optimized ROI Image Compression. IEEE Transactions on Image Processing 29 (2020), 3442–3457. https://doi.org/10.1109/TIP.2019.2960869
  33. Optimized Transcoding for Large Scale Adaptive Streaming Using Playback Statistics. In ICIP 2018. 3269–3273. https://doi.org/10.1109/ICIP.2018.8451307
  34. Liv(e)-ing on the Edge: User-Uploaded Live Streams Driven by ”First-Mile” Edge Decisions. In EDGE 2019. 41–50. https://doi.org/10.1109/EDGE.2019.00023
  35. Yue Chen et al. 2018a. An Overview of Core Coding Tools in the AV1 Video Codec. In PCS 2018. 41–45. https://doi.org/10.1109/PCS.2018.8456249
  36. Ying Chen et al. 2021. Higher Quality Live Streaming Under Lower Uplink Bandwidth: An Approach of Super-Resolution Based Video Coding. NOSSDAV 2021, 75–81. https://doi.org/10.1145/3458306.3458874
  37. Dah-Ming Chiu and Raj Jain. 1989. Analysis of the Increase and Decrease Algorithms for Congestion Avoidance in Computer Networks. Journal of Computer Networks and ISDN 17, 1 (1989), 1–14. https://doi.org/10.1016/0169-7552(89)90019-6
  38. An Overview of the MPEG-5 Essential Video Coding Standard [Standards in a Nutshell]. IEEE Signal Processing Magazine 37, 3 (2020), 160–167. https://doi.org/10.1109/MSP.2020.2971765
  39. Cisco. 2019. Cisco Visual Networking Index: Forecast and Trends, 2017-2022. White Paper C11-741490-00. https://branden.biz/wp-content/uploads/2018/12/Cisco-Visual-Networking-Index_Forecast-and-Trends_2017_2022.pdf.
  40. Cisco. 2020. Cisco Annual Internet Report (2018-2023). White Paper C11-741490-00. https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html.
  41. Conviva. 2020. Conviva’s State of Streaming Q4 2019. Report, https://www.conviva.com/state-of-streaming/convivas-state-of-streaming-q4-2019/.
  42. Conviva. 2021. Conviva’s State of Streaming Q2 2021. Report, https://www.conviva.com/state-of-streaming/convivas-state-of-streaming-q2-2021/.
  43. Conviva. 2022. Conviva’s State of Streaming Q2 2022. Report, https://www.conviva.com/wp-content/uploads/2022/09/Q2-SoS.pdf.
  44. MAMUT: Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-User Video Transcoding. In DATE 2019. 558–563. https://doi.org/10.23919/DATE.2019.8715256
  45. Swift: Adaptive Video Streaming with Layered Neural Codecs. In NSDI 2022. 103–118. https://www.usenix.org/conference/nsdi22/presentation/dasari
  46. ERUDITE: A Deep Neural Network for Optimal Tuning of Adaptive Video Streaming Controllers. In MMSys 2019. 13–24. https://doi.org/10.1145/3304109.3306216
  47. Cracking Open the Black Box: What Observations Can Tell Us about Reinforcement Learning Agents. In NetAI 2019. 29–36. https://doi.org/10.1145/3341216.3342210
  48. Globally Distributed Content Delivery. IEEE Internet Computing 6, 5 (2002), 50–58. https://doi.org/10.1109/MIC.2002.1036038
  49. The H.264 Video Coding Standard. IEEE Potentials 33, 2 (2014), 32–38. https://doi.org/10.1109/MPOT.2013.2284525
  50. Image Super-Resolution Using Deep Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 38, 2 (2015), 295–307. https://doi.org/10.1109/TPAMI.2015.2439281
  51. QoE-Aware Adaptive Bitrate Video Streaming over Mobile Networks with Caching Proxy. In ICNC 2015. 737–741. https://doi.org/10.1109/ICCNC.2015.7069438
  52. Server-Driven Video Streaming for Deep Learning Inference. In SIGCOMM 2020. 557–570. https://doi.org/10.1145/3387514.3405887
  53. Metaverse for Social Good: A University Campus Prototype. In MM 2021. 153–161. https://doi.org/10.1145/3474085.3479238
  54. Evaluating the Performance of Apple’s Low-Latency HLS. In MMSP 2020. 1–6. https://doi.org/10.1109/MMSP48831.2020.9287117
  55. LwTE: Light-Weight Transcoding at the Edge. IEEE Access 9 (2021), 112276–112289. https://doi.org/10.1109/ACCESS.2021.3102633
  56. Nagabhushan Eswara et al. 2020. Streaming Video QoE Modeling and Prediction: A Long Short-Term Memory Approach. IEEE Transactions on Circuits and Systems for Video Technology 30, 3 (2020), 661–673. https://doi.org/10.1109/TCSVT.2019.2895223
  57. On the Merits of SVC-Based HTTP Adaptive Streaming. In IM 2013. 419–426. https://ieeexplore.ieee.org/document/6573013/footnotes#footnotes
  58. On Interpretability of Artificial Neural Networks: A Survey. IEEE Transactions on Radiation and Plasma Medical Sciences 5, 6 (2021), 741–760. https://doi.org/10.1109/TRPMS.2021.3066428
  59. Multimedia Streaming in Information-Centric Networking: A Survey and Future Perspectives. Computer Networks 125 (2017), 103–121. https://doi.org/10.1016/j.comnet.2017.05.030
  60. Vabis: Video Adaptation Bitrate System for Time-Critical Live Streaming. IEEE Transactions on Multimedia 22, 11 (2020), 2963–2976. https://doi.org/10.1109/TMM.2019.2962313
  61. Guanyu Gao et al. 2018. Optimizing Quality of Experience for Adaptive Bitrate Streaming via Viewer Interest Inference. IEEE Transactions on Multimedia 20, 12 (2018), 3399–3413. https://doi.org/10.1109/TMM.2018.2838330
  62. Personalized QoE Improvement for Networking Video Service. IEEE Journal on Selected Areas in Communications 38, 10 (2020), 2311–2323. https://doi.org/10.1109/JSAC.2020.3000395
  63. Cost-Efficient HEVC-Based Quadtree Splitting (HEQUS) for VVC Video Transcoding. Signal Processing: Image Communication 94, article 116199 (2021). https://doi.org/10.1016/j.image.2021.116199
  64. Ehab Ghabashneh and Sanjay Rao. 2020. Exploring the Interplay Between CDN Caching and Video Streaming Performance. In INFOCOM 2020. 516–525. https://doi.org/10.1109/INFOCOM41043.2020.9155338
  65. Torkel Glad and Lennart Ljung. 2017. Control Theory. CRC Press. https://doi.org/10.1201/9781315274737
  66. Michael Grafl et al. 2013. Evaluation of Hybrid Scalable Video Coding for HTTP-Based Adaptive Media Streaming with High-Definition Content. In WoWMoM 2013. 1–7. https://doi.org/10.1109/WoWMoM.2013.6583506
  67. Fast HEVC Transrating Using Random Forests. In VCIP 2018. 1–4. https://doi.org/10.1109/VCIP.2018.8698675
  68. Performance Comparison of Emerging EVC and VVC Video Coding Standards with HEVC and AV1. SMPTE Motion Imaging Journal 130, 4 (2021), 1–12. https://doi.org/10.5594/JMI.2021.3065442
  69. Performance Comparison of H.265/MPEG-HEVC, VP9, and H.264/MPEG-AVC Encoders. In PCS 2013. 394–397. https://doi.org/10.1109/PCS.2013.6737766
  70. A Survey of Actor-Critic Reinforcement Learning: Standard and Natural Policy Gradients. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 42, 6 (2012), 1291–1307. https://doi.org/10.1109/TSMCC.2012.2218595
  71. Reconstructing Proprietary Video Streaming Algorithms. In USENIX ATC 2020. 529–542. https://www.usenix.org/conference/atc20/presentation/gruener
  72. STALLION: Video Adaptation Algorithm for Low-Latency Video Streaming. In MMSys 2020. 327–332. https://doi.org/10.1145/3339825.3397044
  73. Tobias Hoßfeld and Christian Keimel. 2014. Crowdsourcing in QoE Evaluation. In Quality of Experience: Advanced Concepts, Applications and Methods. 315–327. https://doi.org/10.1007/978-3-319-02681-7_21
  74. Learning Anytime Predictions in Neural Networks via Adaptive Loss Balancing. arXiv:1708.06832. https://doi.org/10.48550/arXiv.1708.06832
  75. Shenghong Hu et al. 2019. Affective Content-Aware Adaptation Scheme on QoE Optimization of Adaptive Streaming over HTTP. ACM Transactions on Multimedia Computing, Communications and Applications 15, article 100 (2019), 1–18. https://doi.org/10.1145/3328997
  76. Siqi Huang and Jiang Xie. 2021. DAVE: Dynamic Adaptive Video Encoding for Real-Time Video Streaming Applications. In SECON 2021. 1–9. https://doi.org/10.1109/secon52354.2021.9491588
  77. Tianchi Huang et al. 2020a. Quality-Aware Neural Adaptive Video Streaming with Lifelong Imitation Learning. IEEE Journal on Selected Areas in Communications 38, 10 (2020), 2324–2342. https://doi.org/10.1109/JSAC.2020.3000363
  78. Tiyuntsong: A Self-Play Reinforcement Learning Approach for ABR Video Streaming. In ICME 2019. 1678–1683. https://doi.org/10.1109/ICME.2019.00289
  79. Deep Reinforced Bitrate Ladders for Adaptive Video Streaming. In NOSSDAV 2021. 66–73. https://doi.org/10.1145/3458306.3458873
  80. Optimizing Adaptive Video Streaming with Human Feedback. In MM 2023. 1707–1718. https://doi.org/10.1145/3581783.3611771
  81. Stick: A Harmonious Fusion of Buffer-Based and Learning-Based Approach for Adaptive Streaming. In INFOCOM 2020. 1967–1976. https://doi.org/10.1109/INFOCOM41043.2020.9155411
  82. Hindsight: Evaluate Video Bitrate Adaptation at Scale. In MMSys 2019. 86–97. https://doi.org/10.1145/3304109.3306219
  83. A Buffer-Based Approach to Rate Adaptation: Evidence from a Large Video Streaming Service. In SIGCOMM 2014. 187–198. https://doi.org/10.1145/2740070.2626296
  84. Liangyu Huo et al. 2020. A Meta-Learning Framework for Learning Multi-User Preferences in QoE Optimization of DASH. IEEE Transactions on Circuits and Systems for Video Technology 30, 9 (2020), 3210–3225. https://doi.org/10.1109/TCSVT.2019.2939282
  85. Quan Huynh-Thu and Mohammed Ghanbari. 2008. Scope of Validity of PSNR in Image/Video Quality Assessment. Electronics Letters 44, 13 (2008), 800–801. https://doi.org/10.1049/el:20080522
  86. International Organization for Standardization. 2020. Information-Technology – Multimedia Application Format (MPEG-A) – Part 19: Common Media Application Format (CMAF) for Segmented Media. Standard ISO/IEC 23000-19:2020. https://www.mpeg.org/standards/MPEG-A/19/.
  87. International Telecommunication Union. 2017a. Parametric Bitstream-Based Quality Assessment of Progressive Download and Adaptive Audiovisual Streaming Services over Reliable Transport, Amendment 1. Recommendation P.1203. https://www.itu.int/rec/T-REC-P.1203.
  88. International Telecommunication Union. 2017b. Vocabulary for Performance, Quality of Service and Quality of Experience. Recommendation P.10. https://www.itu.int/rec/T-REC-P.10-201711-I/en.
  89. International Telecommunication Union. July 2016. Mean Opinion Score Interpretation and Reporting. Recommendation P.800.2. https://www.itu.int/rec/T-REC-P.800.2.
  90. Music Personalization at Spotify. In RecSys 2016. 373. https://doi.org/10.1145/2959100.2959120
  91. Performance Evaluation of WebRTC-Based Video Conferencing. ACM SIGMETRICS Performance Evaluation Review 45, 3 (2018), 56–68. https://doi.org/10.1145/3199524.3199534
  92. Improving Fairness, Efficiency, and Stability in HTTP-Based Adaptive Video Streaming with FESTIVE. IEEE/ACM Transactions on Networking 22, 1 (2014), 326–340. https://doi.org/10.1109/TNET.2013.2291681
  93. Image and Video Compression: Fundamentals, Techniques, and Applications. CRC Press. https://doi.org/10.1201/b17738
  94. SARA: Segment Aware Rate Adaptation Algorithm for Dynamic Adaptive Streaming over HTTP. In ICCW 2015. 1765–1770. https://doi.org/10.1109/ICCW.2015.7247436
  95. Measurement of Quality of Experience of Video-on-Demand Services: A Survey. IEEE Communications Surveys Tutorials 18, 1 (2016), 401–418. https://doi.org/10.1109/COMST.2015.2401424
  96. Anton S. Kaplanyan et al. 2019. DeepFovea: Neural Reconstruction for Foveated Rendering and Video Compression Using Learned Statistics of Natural Videos. ACM Transactions on Graphics 38, 6 (2019), 1–13. https://doi.org/10.1145/3355089.3356557
  97. Super Resolution of Images and Video. Springer. https://doi.org/10.2200/S00036ED1V01Y200606IVM007
  98. Ioannis Katsavounidis. 2018. Dynamic Optimizer – A Perceptual Video Encoding Optimization Framework. Technology Blog, Netflix, https://netflixtechblog.com/dynamic-optimizer-a-perceptual-video-encoding-optimization-framework-e19f1e3a277f.
  99. A Subjective Comparison of AV1 and HEVC for Adaptive Video Streaming. In ICIP 2019. 4145–4149. https://doi.org/10.1109/ICIP.2019.8803523
  100. Neural-Enhanced Live Streaming: Improving Live Video Ingest via Online Learning. In SIGCOMM 2020. 107–125. https://doi.org/10.1145/3387514.3405856
  101. Balancing Quality of Experience and Traffic Volume in Adaptive Bitrate Streaming. IEEE Access 9 (2021), 15530–15547. https://doi.org/10.1109/ACCESS.2021.3052552
  102. Context-Aware Adaptive Bitrate Streaming System. In ICC 2021. 1–7. https://doi.org/10.1109/ICC42927.2021.9500665
  103. RL-Cache: Learning-Based Cache Admission for Content Delivery. IEEE Journal on Selected Areas in Communications 38, 10 (2020), 2372–2385. https://doi.org/10.1109/JSAC.2020.3000415
  104. Optimizing the Video Transcoding Workflow in Content Delivery Networks. In MMSys 2015. 37–48. https://doi.org/10.1145/2713168.2713175
  105. ImageNet Classification with Deep Convolutional Neural Networks. Communications of the ACM 60, 6 (2017), 84–90. https://doi.org/10.1145/3065386
  106. A Survey of Rate Adaptation Techniques for Dynamic Adaptive Streaming over HTTP. IEEE Communications Surveys and Tutorials 19, 3 (2017), 1842–1866. https://doi.org/10.1109/COMST.2017.2685630
  107. When Machine Learning Algorithms Meet User Engagement Parameters to Predict Video QoE. Wireless Personal Communications 116, 3 (2021), 2723–2741. https://doi.org/10.1007/s11277-020-07818-w
  108. Adam Langley et al. 2017. The QUIC Transport Protocol: Design and Internet-Scale Deployment. In SIGCOMM 2017. 183–196. https://doi.org/10.1145/3098822.3098842
  109. Eugene L. Lawler and David E. Wood. 1966. Branch-and-Bound Methods: A Survey. Operations Research 14, 4 (1966), 699–719. https://doi.org/10.1287/opre.14.4.699
  110. Video Quality Adaptation for Limiting Transcoding Energy Consumption in Video Servers. IEEE Access 7 (2019), 126253–126264. https://doi.org/10.1109/ACCESS.2019.2939007
  111. Jong-Seok Lee and Touradj Ebrahimi. 2012. Perceptual Video Compression: A Survey. IEEE Journal of Selected Topics in Signal Processing 6, 6 (2012), 684–697. https://doi.org/10.1109/JSTSP.2012.2215006
  112. A Survey on Cloud-Based Video Streaming Services. Advances in Computers 123 (2021), 193–244. https://doi.org/10.1016/bs.adcom.2021.01.003
  113. Reducto: On-Camera Filtering for Resource-Efficient Real-Time Video Analytics. In SIGCOMM 2020. 359–376. https://doi.org/10.1145/3387514.3405874
  114. High Efficiency Live Video Streaming with Frame Dropping. In ICIP 2020. 1226–1230. https://doi.org/10.1109/ICIP40778.2020.9190683
  115. Probe and Adapt: Rate Adaptation for HTTP Video Streaming at Scale. IEEE Journal on Selected Areas in Communications 32, 4 (2014), 719–733. https://doi.org/10.1109/JSAC.2014.140405
  116. Grad: Learning for Overhead-Aware Adaptive Video Streaming with Scalable Video Coding. In MM 2020. 349–357. https://doi.org/10.1145/3394171.3413512
  117. Salvatore Loreto and Simon Pietro Romano. 2014. Real-Time Communication with WebRTC: Peer-to-Peer in the Browser. O’Reilly Media.
  118. Network-Aware Video Level Encoding for Uplink Adaptive HTTP Streaming. In ICC 2015. 6861–6866. https://doi.org/10.1109/ICC.2015.7249419
  119. CrowdSR: Enabling High-Quality Video Ingest in Crowdsourced Livecast via Super-Resolution. In NOSSDAV 2021. 91–97. https://doi.org/10.1145/3458306.3462170
  120. A Study of Subjective Video Quality at Various Frame Rates. In ICIP 2015. 3407–3411. https://doi.org/10.1109/ICIP.2015.7351436
  121. Bruce M. Maggs and Ramesh K. Sitaraman. 2015. Algorithmic Nuggets in Content Delivery. ACM SIGCOMM CCR 45, 3 (2015), 52–66. https://doi.org/10.1145/2805789.2805800
  122. Neural Adaptive Video Streaming with Pensieve. In SIGCOMM 2017. 197–210. https://doi.org/10.1145/3098822.3098843
  123. Guido Meardi et al. 2020. MPEG-5 Part 2: Low Complexity Enhancement Video Coding (LCEVC): Overview and Performance Evaluation. In Applications of Digital Image Processing XLIII, Vol. 11510, article 115101C. https://doi.org/10.1117/12.2569246
  124. Region-of-Interest-Based Rate Control Scheme for High-Efficiency Video Coding. In ICASSP 2014. 7338–7342. https://doi.org/10.1109/ICASSP.2014.6855025
  125. Fastconv: Fast Learning Based Adaptive Bitrate Algorithm for Video Streaming. In GLOBECOM 2019. 1–6. https://doi.org/10.1109/GLOBECOM38437.2019.9013152
  126. Zili Meng et al. 2021. Practically Deploying Heavyweight Adaptive Bitrate Algorithms with Teacher-Student Learning. IEEE/ACM Transactions on Networking 29, 2 (2021), 723–736. https://doi.org/10.1109/TNET.2020.3048666
  127. QoE-Based Low-Delay Live Streaming Using Throughput Predictions. ACM Transactions on Multimedia Computing, Communications, and Applications 13, 1, article 4 (2016). https://doi.org/10.1145/2990505
  128. Bamshad Mobasher. 2007. Data Mining for Web Personalization. In The Adaptive Web: Methods and Strategies of Web Personalization. 90–135. https://doi.org/10.1007/978-3-540-72079-9
  129. Christian Moldovan and Florian Metzger. 2016. Bridging the Gap Between QoE and User Engagement in HTTP Video Streaming. In ITC 2016. 103–111. https://doi.org/10.1109/ITC-28.2016.122
  130. Practical, Real-Time Centralized Control for CDN-Based Live Video Delivery. In SIGCOMM 2015. 311–324. https://doi.org/10.1145/2829988.2787475
  131. Towards QoE-Aware Video Streaming Using SDN. In GLOBECOM 2014. 1317–1322. https://doi.org/10.1109/GLOCOM.2014.7036990
  132. End-to-End Transport for Video QoE Fairness. In SIGCOMM 2019. 408–423. https://doi.org/10.1145/3341302.3342077
  133. Michael J. Neely. 2010. Stochastic Network Optimization with Application to Communication and Queueing Systems. Morgan and Claypool. https://doi.org/10.1007/978-3-031-79995-2
  134. Antonopoulos Nikos and Gillam Lee. 2010. Cloud Computing: Principles, Systems and Applications. Springer. https://doi.org/10.1007/978-3-319-54645-2
  135. The QUIC Fix for Optimal Video Streaming. In EPIQ 2018. 43–49. https://doi.org/10.1145/3284850.3284857
  136. Haitian Pang et al. 2019. Content Harvest Network: Optimizing First Mile for Crowdsourced Live Streaming. IEEE Transactions on Circuits and Systems for Video Technology 29, 7 (2019), 2112–2125. https://doi.org/10.1109/TCSVT.2018.2862619
  137. Roger Pantos and William May. 2017. HTTP Live Streaming. August 2017, RFC 8216, IETF, https://www.rfc-editor.org/info/rfc8216.
  138. Kyoungjun Park and Myungchul Kim. 2019. EVSO: Environment-Aware Video Streaming Optimization of Power Consumption. In INFOCOM 2019. 973–981. https://doi.org/10.1109/INFOCOM.2019.8737392
  139. A Network-Aware Encoding Rate Control Algorithm for Real-Time Up-Streaming Video Services. IEEE Communications Letters 21, 7 (2017), 1653–1656. https://doi.org/10.1109/LCOMM.2017.2691674
  140. Hardeep Singh Parmar and Michael C. Thornburgh. 2012. Adobe’s Real Time Messaging Protocol. Memorandum, Adobe. https://rtmp.veriskope.com/pdf/rtmp_specification_1.0.pdf.
  141. Mukaddim Pathan and Rajkumar Buyya. 2008. A Taxonomy of CDNs. Springer. https://doi.org/10.1007/978-3-540-77887-5_2
  142. Leonardo Peroni and Sergey Gorinsky. 2024. Quality of Experience in Video Streaming: Status Quo, Pitfalls, and Guidelines. In COMSNETS 2024. 1–10. https://dspace.networks.imdea.org/handle/20.500.12761/1767
  143. Empowerment of Atypical Viewers via Low-Effort Personalized Modeling of Video Streaming Quality. Proceedings of the ACM on Networking 1, CoNEXT3 (2023), 1–27. https://doi.org/10.1145/3629139
  144. Towards the Prediction of the Quality of Experience from Facial Expression and Gaze Direction. In ICIN 2019. 82–87. https://doi.org/10.1109/ICIN.2019.8685917
  145. A Survey on Deep Learning: Algorithms, Techniques, and Applications. ACM Computing Surveys 51, 5 (2018), 1–36. https://doi.org/10.1145/3234150
  146. Beyond QoE: Diversity Adaptation in Video Streaming at the Edge. IEEE/ACM Transactions on Networking 29, 1 (2021), 289–302. https://doi.org/10.1109/TNET.2020.3032416
  147. Yanyuan Qin et al. 2017. A Control Theoretic Approach to ABR Video Streaming: A Fresh Look at PID-Based Rate Adaptation. In INFOCOM 2017. 1–9. https://doi.org/10.1109/INFOCOM.2017.8057056
  148. Quality-Aware Strategies for Optimizing ABR Video Streaming QoE and Reducing Data Usage. In MMSys 2019. 189–200. https://doi.org/10.1145/3304109.3306231
  149. Alexander Raake et al. 2012. IP-Based Mobile and Fixed Network Audiovisual Media Services. IEEE Signal Processing Magazine 29, 6 (2012), 163–163. https://doi.org/10.1109/MSP.2011.942472
  150. SSR: Joint Optimization of Recommendation and Adaptive Bitrate Streaming for Short-Form Video Feed. In MSN 2020. 418–426. https://doi.org/10.1109/MSN50589.2020.00074
  151. Vantage: Optimizing Video Upload for Time-Shifted Viewing of Social Live Streams. In SIGCOMM 2019. 380–393. https://doi.org/10.1145/3341302.3342064
  152. Optimizing Mass-Scale Multi-Screen Video Delivery. SMPTE Motion Imaging Journal 129, 3 (2020), 26–38. https://doi.org/10.5594/JMI.2020.2973561
  153. Adaptive Bitrate Selection: A Survey. IEEE Communications Surveys and Tutorials 19, 4 (2017), 2985–3014. https://doi.org/10.1109/COMST.2017.2725241
  154. SMASH: A Supervised Machine Learning Approach to Adaptive Video Streaming over HTTP. In QoMEX 2020. 2–7. https://doi.org/10.1109/QoMEX48832.2020.9123139
  155. Overview of the Scalable Video Coding Extension of the H.264/AVC Standard. IEEE Transactions on Circuits and Systems for Video Technology 17, 9 (2007), 1103–1120. https://doi.org/10.1109/TCSVT.2007.905532
  156. QUICker or Not? – An Empirical Analysis of QUIC vs TCP for Video Streaming QoE Provisioning. In ICIN 2019. 7–12. https://doi.org/10.1109/ICIN.2019.8685913
  157. Effectiveness of Crypto-Transcoding for H.264/AVC and HEVC Video Bit-Streams. Multimedia Tools and Applications 78, 15 (2019), 21455–21484. https://doi.org/10.1007/s11042-019-7451-5
  158. A New Rate Control Algorithm Based on Region of Interest for HEVC. In PCM 2016. 571–579. https://doi.org/10.1007/978-3-319-48896-7_56
  159. LEAP: Learning-Based Smart Edge with Caching and Prefetching for Adaptive Video Streaming. In IWQoS 2019. 1–10. https://doi.org/10.1145/3326285.3329051
  160. Optimized Upload Strategies for Live Scalable Video Transmission from Mobile Devices. IEEE Transactions on Mobile Computing 16, 4 (2017), 1059–1072. https://doi.org/10.1109/TMC.2016.2585138
  161. Lea Skorin-Kapov and Martín Varela. 2012. A Multi-Dimensional View of QoE: The ARCU Model. In MIPRO 2012. 662–666. https://ieeexplore.ieee.org/document/6240728
  162. PREPARE – Playback Rate and Priority Adaptive Bitrate Selection. IEEE Access 7 (2019), 135352–135362. https://doi.org/10.1109/ACCESS.2019.2941051
  163. BOLA: Near-Optimal Bitrate Adaptation for Online Videos. IEEE/ACM Transactions on Networking 28, 4 (2020), 1698–1711. https://doi.org/10.1109/TNET.2020.2996964
  164. WebRTC Technology Overview and Signaling Solution Design and Implementation. In MIPRO 2015. 1006–1009. https://doi.org/10.1109/MIPRO.2015.7160422
  165. Thomas Stockhammer. 2011. Dynamic Adaptive Streaming over HTTP – Standards and Design Principle. In MMSys 2011. 133–143. https://doi.org/10.1145/1943552.1943572
  166. Overview of the High Efficiency Video Coding (HEVC) Standard. IEEE Transactions on Circuits and Systems for Video Technology 22, 12 (2012), 1649–1668. https://doi.org/10.1109/TCSVT.2012.2221191
  167. Beyond 1 Million Nodes: A Crowdsourced Video Content Delivery Network. IEEE MultiMedia 24, 3 (2017), 54–63. https://doi.org/10.1109/MMUL.2017.3051522
  168. Towards Optimal Low-Latency Live Video Streaming. IEEE/ACM Transactions on Networking 29, 5 (2021), 2327–2338. https://doi.org/10.1109/TNET.2021.3087625
  169. Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. MIT Press.
  170. Wowza Media Systems. 2019. Video Streaming Latency Report. September 2019, Report, https://www.wowza.com/wp-content/uploads/Streaming-Video-Latency-Report-Interactive-2019.pdf.
  171. ARTEMIS: Adaptive Bitrate Ladder Optimization for Live Video Streaming. In NSDI 2024. https://dspace.networks.imdea.org/handle/20.500.12761/1760
  172. Alexander Thomasian. 2021. Storage Systems: Organization, Performance, Coding, Reliability, and Their Data Processing. Elsevier. https://doi.org/10.1016/C2020-0-04492-0
  173. Performance Comparison of VVC, AV1, and HEVC on 8-Bit and 10-Bit Content. In Applications of Digital Image Processing XLI, Vol. 10752, article 107520V. https://doi.org/10.1117/12.2322024
  174. Roberto Viola et al. 2021. Predictive CDN Selection for Video Delivery Based on LSTM Network Performance Forecasts and Cost-Effective Trade-Offs. IEEE Transactions on Broadcasting 67, 1 (2021), 145–158. https://doi.org/10.1109/TBC.2020.3031724
  175. Comparing Cloud Content Delivery Networks for Adaptive Video Streaming. In CLOUD 2018. 686–693. https://doi.org/10.1109/CLOUD.2018.00094
  176. SQUAD: A Spectrum-Based Quality Adaptation for Dynamic Adaptive Streaming over HTTP. In MMSys 2016. 1–12. https://doi.org/10.1145/2910017.2910593
  177. Image Quality Assessment: From Error Visibility to Structural Similarity. IEEE Transactions on Image Processing 13 (2004), 600–612. https://doi.org/10.1109/TIP.2003.819861
  178. Deep Learning for Image Super-Resolution: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 10 (2021), 3365–3387. https://doi.org/10.1109/TPAMI.2020.2982166
  179. Leveraging Transitions for the Upload of User-Generated Mobile Video. In MoVid 2016. 25–30. https://doi.org/10.1145/2910018.2910658
  180. Wowza Media Systems. 2021. Video Streaming Latency Report. September 2021, Report, https://www.wowza.com/blog/2021-video-streaming-latency-report#.
  181. Rate Control in Video Coding. In Recent Advances on Video Coding. https://doi.org/10.5772/14585
  182. Karma: Adaptive Video Streaming via Causal Sequence Modeling. In MM 2023. 1527–1535. https://doi.org/10.1145/3581783.3612177
  183. Video Analytics with Zero-streaming Cameras. In USENIX ATC 2021. 459–472. https://www.usenix.org/conference/atc21/presentation/xu
  184. FedABR: A Personalized Federated Reinforcement Learning Approach for Adaptive Video Streaming. In Networking 2023. 1–9. https://doi.org/10.23919/IFIPNetworking57963.2023.10186404
  185. QUETRA: A Queuing Theory Approach to DASH Rate Adaptation. In MM 2017. 1130–1138. https://doi.org/10.1145/3123266.3123390
  186. Learning in Situ: A Randomized Experiment in Video Streaming. In NSDI 2020. 495–511. https://www.usenix.org/conference/nsdi20/presentation/yan
  187. Neural Adaptive Content-Aware Internet Video Delivery. In OSDI 2018. 645–661. https://www.usenix.org/conference/osdi18/presentation/yeo
  188. NeuroScaler: Neural Video Enhancement at Scale. In SIGCOMM 2022. 795–811. https://doi.org/10.1145/3544216.3544218
  189. ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming. arXiv:2104.12507. https://doi.org/10.48550/ARXIV.2104.12507
  190. A Control-Theoretic Approach for Dynamic Adaptive Video Streaming over HTTP. In SIGCOMM 2015. 325–338. https://doi.org/10.1145/2785956.2787486
  191. Motion-Homogeneous-Based Fast Transcoding Method from H.264/AVC to HEVC. IEEE Transactions on Multimedia 19, 7 (2017), 1416–1430. https://doi.org/10.1109/TMM.2017.2669858
  192. Ahmed Hamdy Zahran et al. 2016. OSCAR: An Optimized Stall-Cautious Adaptive Bitrate Streaming Algorithm for Mobile Networks. In MoVid 2016. 1–6. https://doi.org/10.1145/2910018.2910655
  193. ARBITER+: Adaptive Rate-Based Intelligent HTTP Streaming Algorithm for Mobile Networks. IEEE Transactions on Mobile Computing 17, 12 (2018), 2716–2728. https://doi.org/10.1109/TMC.2018.2825384
  194. Aoyang Zhang et al. 2021a. Video Super-Resolution and Caching – An Edge-Assisted Adaptive Video Streaming Solution. IEEE Transactions on Broadcasting 67, 4 (2021), 799–812. https://doi.org/10.1109/TBC.2021.3071010
  195. Towards Influence of Chunk Size Variation on Video Streaming in Wireless Networks. IEEE Transactions on Mobile Computing 19, 7 (2020), 1715–1730. https://doi.org/10.1109/TMC.2019.2912750
  196. SENSEI: Aligning Video Streaming Quality with Dynamic User Sensitivity. In NSDI 2021. 303–320. https://www.usenix.org/conference/nsdi21/presentation/zhang-xu
  197. Enabling Personalized Video Quality Optimization with VidHoc. arXiv:2211.15959. https://doi.org/10.48550/arXiv.2211.15959
  198. Yinjie Zhang et al. 2020a. Improving Quality of Experience by Adaptive Video Streaming with Super-Resolution. In INFOCOM 2020. 1957–1966. https://doi.org/10.1109/INFOCOM41043.2020.9155384
  199. QoE in Video Transmission: A User Experience-Driven Strategy. IEEE Communications Surveys and Tutorials 19, 1 (2017), 285–302. https://doi.org/10.1109/COMST.2016.2619982
  200. Shiping Zhu and Ziyao Xu. 2017. Spatiotemporal Visual Saliency Guided Perceptual High Efficiency Video Coding with Neural Network. Neurocomputing 275 (2017), 511–522. https://doi.org/10.1016/j.neucom.2017.08.054
  201. Yi Zhu et al. 2018. Measuring Individual Video QoE: A Survey, and Proposal for Future Directions Using Social Media. ACM Transactions on Multimedia Computing, Communications and Applications 14, 2s, article 30 (2018), 1–24. https://doi.org/10.1145/3183512
  202. Behrouz Zolfaghari et al. 2020. Content Delivery Networks: State of the Art, Trends, and Future Roadmap. ACM Computing Surveys 53, 2 (2020), 1–34. https://doi.org/10.1145/3380613
  203. Adaptive Bitrate with User-Level QoE Preference for Video Streaming. In INFOCOM 2022. 1279–1288. https://doi.org/10.1109/INFOCOM48880.2022.9796953
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