Abstract
HTTP adaptive streaming with chunked transfer encoding can offer low-latency streaming without sacrificing the coding efficiency. This allows media segments to be delivered while still being packaged. However, conventional schemes often make widely inaccurate bandwidth measurements due to the presence of idle periods between the chunks and hence this is causing sub-optimal adaptation decisions. To address this issue, we earlier proposed ACTE (ABR for Chunked Transfer Encoding) [6], a bandwidth prediction scheme for low-latency chunked streaming. While ACTE was a significant step forward, in this study we focus on two still remaining open areas, namely, (i) quantifying the impact of encoding parameters, including chunk and segment durations, bitrate levels, minimum interval between IDR-frames and frame rate on ACTE, and (ii) exploring the impact of video content complexity on ACTE. We thoroughly investigate these questions and report on our findings. We also discuss some additional issues that arise in the context of pursuing very low latency HTTP video streaming.
References
- ISO/IEC. 2018. Information Technology—Multimedia Application Format (MPEG-A)—Part 19: Common Media Application Format (CMAF) for Segmented Media. Standard ISO/IEC 23000-19:2018. ISO/IEC, Geneva, CH. Retrieved from https://www.iso.org/standard/71975.html.Google Scholar
- Apple. 2019. Protocol Extension for Low-Latency HLS. Retrieved from https://developer.apple.com/documentation/http_live_streaming/protocol_extension_for_low-latency_hls_preliminary_specification.Google Scholar
- A. C. Begen and Y. Syed. 2018. Are the streaming format wars over? In Proceedings of the IEEE International Conference on Multimedia Expo Workshops (ICMEW’18). 1--4. DOI:https://doi.org/10.1109/ICMEW.2018.8551563Google Scholar
- Abdelhak Bentaleb, Ali C. Begen, Saad Harous, and Roger Zimmermann. 2018. Want to play DASH? A game theoretic approach for adaptive streaming over HTTP. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys’18). ACM, New York, NY, 13--26. DOI:https://doi.org/10.1145/3204949.3204961Google Scholar
Digital Library
- A. Bentaleb, B. Taani, A. C. Begen, C. Timmerer, and R. Zimmermann. 2019. A survey on bitrate adaptation schemes for streaming media over HTTP. IEEE Commun. Surveys Tutor. 21, 1 (2019), 562--585.Google Scholar
Cross Ref
- Abdelhak Bentaleb, Christian Timmerer, Ali C. Begen, and Roger Zimmermann. 2019. 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 (NOSSDAV’19). ACM, New York, NY, 7--13. DOI:https://doi.org/10.1145/3304112.3325611Google Scholar
Digital Library
- N. Bouzakaria, C. Concolato, and J. Le Feuvre. 2014. Overhead and performance of low latency live streaming using MPEG-DASH. In Proceedings of the 5th International Conference on Information, Intelligence, Systems and Applications. 92--97. DOI:https://doi.org/10.1109/IISA.2014.6878732Google Scholar
- DASH-IF and DVB. 2019. Low-latency Modes for DASH. Retrieved from https://dashif.org/docs/DASH-IF-IOP-CR-Low-Latency-Live-Community-Review.pdf.Google Scholar
- DASH Industry Forum (DASH-IF). 2019. dash.js JavaScript Reference Client. Retrieved from https://reference.dashif.org/dash.js/.Google Scholar
- A. E. Essaili, T. Lohmar, and M. Ibrahim. 2018. Realization and evaluation of an end-to-end low latency live DASH system. In Proceedings of the IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB’18). 1--5. DOI:https://doi.org/10.1109/BMSB.2018.8436922Google Scholar
- R. J. Haddad, M. P. McGarry, and P. Seeling. 2013. Video bandwidth forecasting. IEEE Commun. Surveys Tutor. 15, 4 (2013), 1803--1818. DOI:https://doi.org/10.1109/SURV.2013.032213.00091Google Scholar
Cross Ref
- Simon S. Haykin. 2008. Adaptive Filter Theory. Pearson Education India.Google Scholar
- P. Houzé, E. Mory, G. Texier, and G. Simon. 2016. Applicative-layer multipath for low-latency adaptive live streaming. In Proceedings of the IEEE International Conference on Communications (ICC’16). 1--7. DOI:https://doi.org/10.1109/ICC.2016.7511550Google Scholar
- Xinjue Hu, Wei Quan, Tao Guo, Yu Liu, and Lin Zhang. 2019. Mobile edge assisted live streaming system for omnidirectional video. Mobile Info. Syst. 2019 (2019).Google Scholar
- Te-Yuan Huang, Ramesh Johari, Nick McKeown, Matthew Trunnell, and Mark Watson. 2014. A buffer-based approach to rate adaptation: Evidence from a large video streaming service. In Proceedings of the Association for Computing Machinery’s Special Interest Group on Data Communications (SIGCOMM’14). Association for Computing Machinery, New York, NY, 187--198. DOI:https://doi.org/10.1145/2619239.2626296Google Scholar
Digital Library
- Bitmovin Inc. 2019. Video Developer Report. Retrieved from https://bitmovin.com/bitmovin-2019-video-developer-report-av1-codec-ai-machine-learning-low-latency/.Google Scholar
- Will L. [n.d.]. Ultra-Low-Latency Streaming Using Chunked-Encoded and Chunked-Transferred CMAF. Akamai White paper. Retrieved from https://www.akamai.com/us/en/multimedia/documents/white-paper/low-latency-streaming-cmaf-whitepaper.pdf.Google Scholar
- Z. Li, X. Zhu, J. Gahm, R. Pan, H. Hu, A. C. Begen, and D. Oran. 2014. Probe and adapt: Rate adaptation for HTTP video streaming at scale. IEEE J. Select. Areas Commun. 32, 4 (Apr. 2014), 719--733. DOI:https://doi.org/10.1109/JSAC.2014.140405Google Scholar
Cross Ref
- Hongzi Mao, Ravi Netravali, and Mohammad Alizadeh. 2017. Neural adaptive video streaming with pensieve. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication (SIGCOMM’17). ACM, New York, NY, 197--210. DOI:https://doi.org/10.1145/3098822.3098843Google Scholar
Digital Library
- F. Musumeci, C. Rottondi, A. Nag, I. Macaluso, D. Zibar, M. Ruffini, and M. Tornatore. 2019. An overview on application of machine learning techniques in optical networks. IEEE Commun. Surveys Tutor. 21, 2 (2019), 1383--1408. DOI:https://doi.org/10.1109/COMST.2018.2880039Google Scholar
Cross Ref
- R. K. Naha, S. Garg, D. Georgakopoulos, P. P. Jayaraman, L. Gao, Y. Xiang, and R. Ranjan. 2018. Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE Access 6 (2018), 47980--48009. DOI:https://doi.org/10.1109/ACCESS.2018.2866491Google Scholar
Digital Library
- Michael J. Neely. 2010. Queue stability and probability 1 convergence via lyapunov optimization. arXiv preprint arXiv:1008.3519.Google Scholar
- H. Pang, C. Zhang, F. Wang, J. Liu, and L. Sun. 2019. Towards low latency multi-viewpoint 360° interactive video: A multimodal deep reinforcement learning approach. In Proceedings of the IEEE Conference on Computer Communications. 991--999. DOI:https://doi.org/10.1109/INFOCOM.2019.8737395Google Scholar
- R. Pantos. 2019. HTTP Live Streaming 2nd Edition. Retrieved from https://datatracker.ietf.org/doc/draft-pantos-hls-rfc8216bis/.Google Scholar
- Twitter Periscope. 2018. Introducing LHLS Media Streaming. Retrieved from https://medium.com/@periscopecode/introducing-lhls-media-streaming-eb6212948bef.Google Scholar
- Darijo Raca, Ahmed H. Zahran, Cormac J. Sreenan, Rakesh K. Sinha, Emir Halepovic, Rittwik Jana, Vijay Gopalakrishnan, Balagangadhar Bathula, and Matteo Varvello. 2019. Empowering video players in cellular: Throughput prediction from radio network measurements. In Proceedings of the 10th ACM Multimedia Systems Conference (MMSys’19). ACM, New York, NY, 201--212. DOI:https://doi.org/10.1145/3304109.3306233Google Scholar
Digital Library
- W. Robitza, S. Göring, A. Raake, D. Lindegren, G. Heikkilä, J. Gustafsson, P. List, B. Feiten, U. Wüstenhagen, M.-N. Garcia, K. Yamagishi, and S. Broom. 2018. HTTP adaptive streaming QoE estimation with ITU-T Rec. P. 1203: Open databases and software. In Proceedings of the 9th ACM Multimedia Systems Conference (MMSys’18). ACM, New York, NY, 466--471. DOI:https://doi.org/10.1145/3204949.3208124Google Scholar
- Y. Shuai and T. Herfet. 2018. Towards reduced latency in adaptive live streaming. In Proceedings of the 15th IEEE Annual Consumer Communications Networking Conference (CCNC’18). 1--4. DOI:https://doi.org/10.1109/CCNC.2018.8319262Google Scholar
- I. Sodagar. 2011. The MPEG-DASH standard for multimedia streaming over the internet. IEEE MultiMedia 18, 4 (April 2011), 62--67. DOI:https://doi.org/10.1109/MMUL.2011.71Google Scholar
Digital Library
- K. Spiteri, R. Urgaonkar, and R. K. Sitaraman. 2016. BOLA: Near-optimal bitrate adaptation for online videos. In Proceedings of the 35th Annual IEEE International Conference on Computer Communications. 1--9. DOI:https://doi.org/10.1109/INFOCOM.2016.7524428Google Scholar
- Yi Sun, Xiaoqi Yin, Junchen Jiang, Vyas Sekar, Fuyuan Lin, Nanshu Wang, Tao Liu, and Bruno Sinopoli. 2016. CS2P: Improving video bitrate selection and adaptation with data-driven throughput prediction. In Proceedings of the ACM SIGCOMM Conference (SIGCOMM’16). ACM, New York, NY, 272--285. DOI:https://doi.org/10.1145/2934872.2934898Google Scholar
Digital Library
- V. Swaminathan and S. Wei. 2011. Low latency live video streaming using HTTP chunked encoding. In Proceedings of the IEEE 13th International Workshop on Multimedia Signal Processing. 1--6. DOI:https://doi.org/10.1109/MMSP.2011.6093825Google Scholar
- J. Van Der Hooft, S. Petrangeli, T. Wauters, R. Huysegems, T. Bostoen, and F. De Turck. 2018. An HTTP/2 push-based approach for low-latency live streaming with super-short segments. J. Netw. Syst. Manage. 26, 1 (Jan. 2018), 51--78. DOI:https://doi.org/10.1007/s10922-017-9407-2Google Scholar
- V. Veillon, C. Denninnart, and M. A. Salehi. 2019. F-FDN: Federation of fog computing systems for low latency video streaming. In Proceedings of the IEEE 3rd International Conference on Fog and Edge Computing (ICFEC’19). 1--9. DOI:https://doi.org/10.1109/CFEC.2019.8733154Google Scholar
- Xiufeng Xie, Xinyu Zhang, Swarun Kumar, and Li Erran Li. 2016. piStream: Physical layer informed adaptive video streaming over LTE. GetMobile: Mobile Comp. and Comm. 20, 2 (Oct. 2016), 31--34. DOI:https://doi.org/10.1145/3009808.3009819Google Scholar
Digital Library
- Mariem Ben Yahia, Yannick Le Louedec, Gwendal Simon, Loutfi Nuaymi, and Xavier Corbillon. 2019. HTTP/2-based frame discarding for low-latency adaptive video streaming. ACM Trans. Multimedia Comput. Commun. Appl. 15, 1 (Feb. 2019). DOI:https://doi.org/10.1145/3280854Google Scholar
Digital Library
- X. Yin, A. Jindal, V. Sekar, and B. Sinopoli. 2015. A control-theoretic approach for dynamic adaptive video streaming over HTTP. SIGCOMM Comput. Commun. Rev. 45, 4 (Aug. 2015), 325--338. DOI:https://doi.org/10.1145/2829988.2787486Google Scholar
Digital Library
Index Terms
Performance Analysis of ACTE: A Bandwidth Prediction Method for Low-latency Chunked Streaming





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