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Look ahead to improve QoE in DASH streaming

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When a video is encoded with constant quality, the resulting bitstream will have variable bitrate due to the inherent nature of the video encoding process. This paper proposes a video Adaptive Bitrate Streaming (ABR) algorithm, called Look Ahead, which takes into account this bitrate variability in order to calculate, in real time, the appropriate quality level that minimizes the number of interruptions during the playback. The algorithm is based on the Dynamic Adaptive Streaming over HTTP (DASH) standard for on-demand video services. In fact, it has been implemented and integrated into ExoPlayer v2, the latest version of the library developed by Google to play DASH contents. The proposed algorithm is compared to the Müller and Segment Aware Rate Adaptation (SARA) algorithms as well as to the default ABR algorithm integrated into ExoPlayer. The comparison is carried out by using the most relevant parameters that affect the Quality of Experience (QoE) in video playback services, that is, number and duration of stalls, average quality of the video playback and number of representation switches. These parameters can be combined to define a QoE model. In this sense, this paper also proposes two new QoE models for the evaluation of ABR algorithms. One of them considers the bitrate of every segment of each representation, and the second is based on VMAF (Video Multimethod Assessment Fusion), a Video Quality Assessment (VQA) method developed by Netflix. The evaluations presented in the paper reflect: first, that Look Ahead outperforms the Müller, SARA and the ExoPlayer ABR algorithms in terms of number and duration of video playback stalls, with hardly decreasing the average video quality; and second, that the two QoE models proposed are more accurate than other similar models existing in the literature.

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  1. Akhshabi S, Narayanaswamy S, Begen AC, Dovrolis C (2012) An experimental evaluation of rate-adaptive video players over HTTP. Signal process. Image Commun 27(4):271–287.

    Article  Google Scholar 

  2. Android Developers webpage, ExoPlayer. Available online at: Accessed: Jun. (2019)

  3. Bampis CG, Li Z, Bovik AC (2018) SpatioTemporal feature integration and model fusion for full reference video quality assessment. IEEE Trans on Circuits and Syst for Video Tech 29:2256–2270.

    Article  Google Scholar 

  4. Barman N, Martini MG (2019) QoE modeling for HTTP adaptive video streaming - a survey and open challenges. IEEE Access 7:30831–30859.

    Article  Google Scholar 

  5. Belda R (2013) Algoritmo de adaptación DASH: Look Ahead. Master Thesis. Universitat Politècnica de València.

  6. Belda R, de Fez I, Arce P, Guerri J C (2018) Look ahead: a DASH adaptation algorithm. Proc. of the IEEE Int. Symp. On broadband multimed. Syst. And broadcast., Valencia, Spain: article no. 158.

  7. Blender Foundation webpage. Available online at: Accessed: Jun. (2019).

  8. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20-3:273–297.

    Article  MATH  Google Scholar 

  9. DASH Industry forum webpage. Available online at: Accessed: Jun. (2019)

  10. Ghadiyaram D, Pan J, Bovik AC (2019) A subjective and objective study of stalling events in mobile streaming videos. IEEE Trans on Circuits and Syst for Video Technol 29(1):183–197.

    Article  Google Scholar 

  11. Ghent University. 4G/LTE bandwidth logs. Available online at: Accessed: Jun. (2019).

  12. Github webpage. A DASH segment size aware rate adaptation model for DASH. Available online at: Accessed: Jun. (2019)

  13. GitHub website. Dashgen, Multimedia Communications Group. Available online at: Accessed: Jun. (2019).

  14. van der Hooft J, Petrangeli S, Wauters T, Huysegems R, Alface PR, Bostoen T, De Turck F (2016) HTTP/2-based adaptive streaming of HEVC video over 4G/LTE networks. IEEE Commun Lett 20(1):2177–2180.

    Article  Google Scholar 

  15. Huang TY, Johari R, McKeown N, Trunnell M, Watson M (2014) A buffer-based approach to rate adaptation: evidence from a large video streaming service. Proc. of the 2014 ACM Conf. On SIGCOMM, Chicago, IL, USA: 187-198.

  16. Institute of Telecommunications and Multimedia Applications website. Look Ahead Demo. Available online at: Accessed: Jun. (2019)

  17. ISO/IEC 23009–1:2014 (2014) Dynamic adaptive streaming over HTTP (DASH) - Part 1: media presentation description and segment formats.

  18. Juluri P, Tamarapalli V, Medhi D (2015) SARA: segment aware rate adaptation algorithm for dynamic adaptive streaming over HTTP. Proc. of the IEEE Int. Conf. On Commun. Workshop (ICCW), London, UK: 1765-1770.

  19. Juluri P, Tamarapalli V, Medhi D (2016) QoE management in DASH systems using the segment aware rate adaptation algorithm. Proc. of the IEEE/IFIP Netw. Oper. And Manag. Symp. (NOMS), Istanbul, Turkey: 129-136.

  20. Kua J, Armitage G, Branch P (2017) A survey of rate adaptation techniques for dynamic adaptive streaming over HTTP. IEEE Commun Surv & Tutor 19(3):1842–1866.

    Article  Google Scholar 

  21. Lee S, Youn K, Chung K (2015) Adaptive video quality control scheme to improve QoE of MPEG DASH. Proc. of IEEE Int. Conf. On Consum. Electron. (ICCE), Las Vegas, NV, USA: 126-127.

  22. Li S, Zhang F, Ma L, Ngan K (2011) Image quality assessment by separately evaluating detail losses and additive impairments. IEEE Trans. on Multimed. 13-5:935–949.

    Article  Google Scholar 

  23. Liu C, Bouazizi I, Gabbouj M (2011) Rate adaptation for adaptive HTTP streaming. Proc. of the second annual ACM Conf. On multimed. Syst. (MMSys), San Jose, CA, USA: 169-174.

  24. Medium webpage (2016) Toward a practical perceptual video quality metric. Available online at: Accessed: Jun. 2019.

  25. Mobile Video Service Performance Study (2015) HUAWEI white paper. Available online at:

  26. Mok RKP, Luo X, Chan EWW, Chang RKC (2012) QDASH: a QoE-aware DASH system. Proc. of multim. Syst. Conf. (MMSys), Chapel Hill, NC, USA: 11-22.

  27. Moldovan C, Hagn K, Sieber C, Kellerer W, Hoßfeld T (2017) Keep calm and don’t switch: about the relationship between switches and quality in HAS. Proc. of the Int. Teletraffic Congr. (ITC), Genoa, Italy: pp. 1-6.

  28. Müller C, Lederer S, Timmerer C (2012) An evaluation of dynamic adaptive streaming over HTTP in vehicular environments. Proc. of the 4th workshop on mob. Video (MoVid), Chapel Hill, NC, USA: 37-42.

  29. Nguyen T, Vu T, Nguyen DV, Ngoc NP, and Thang TC (2015) QoE optimization for adaptive streaming with multiple VBR videos. Proc. of the Int. Conf. On comp., Manag. And Telecommun. (ComManTel), DaNang, Vietnam: 189-193.

  30. Qin Y, H. Shuai, Pattipati K R, Qian F, Sen S, Wang B, Yue C (2018) ABR Streaming of VBR-encoded videos: characterization, challenges, and solutions. Proc. of ACM CoNext 2018, Heraklion, Greece: 366–378.

  31. Samain J, Carofiglio G, Muscariello L, Papalini M, Sardara M, Tortelli M, Rossi D (2017) Dynamic adaptive video streaming: towards a systematic comparison of ICN and TCP/IP. IEEE Trans on Multimed 19(10):2166–2181.

    Article  Google Scholar 

  32. Sheikh H, Bovik A (2006) Image information and visual quality. IEEE Trans on Image Process 15(2):430–444.

    Article  Google Scholar 

  33. Shuai Y, Herfet T (2016). A buffer dynamic stabilizer for low-latency adaptive video streaming. Proc. of the Int. Conf. on Consum. Electron., Berlin: 1–5.

  34. Tavakoli S, Egger S, Seufert M, Schatz R, Brunnström K, García N (2016) Perceptual quality of HTTP adaptive streaming strategies: cross-experimental analysis of multi-laboratory and crowdsourced subjective studies. IEEE Journal on Select Areas in Commun 34-8:2141–2153.

    Article  Google Scholar 

  35. Yarnagula H K, Juluri P, Mehr S K, Tamarapalli V, Medhi D (2019) QoE for Mobile clients with segment-aware rate adaptation algorithm (SARA) for DASH video streaming. ACM trans. On multimed. Comput., Commun., and Appl. (TOMM) 15(2):article no. 36

  36. Yin X, Sekar V, Sinopoli B (2014) Toward a principled framework to design dynamic adaptive streaming algorithms over HTTP. Proc. of the 13th ACM workshop on hot topics in Netw. (HotNets), Los Angeles, CA, USA: 1-7.

  37. YouTube webpage (2019) Youtube press. Available online at: Accessed: Jun. 2019.

  38. Youtube webpage, Google I/O ‘18: Building feature-rich media apps with ExoPlayer. Available online at: Published: May (2018)

  39. Yu L, Tillo T, Xiao J (2017) QoE-driven dynamic adaptive video streaming strategy with future information. IEEE Trans on Broadcast 63-3:523–534.

    Article  Google Scholar 

  40. Zhao S, Li Z, Medhi D, Lai P, Liu S (2017) Study of user QoE improvement for dynamic adaptive streaming over HTTP (MPEG-DASH). Proc. of the Int. Conf. On Comput., network. And Commun. (ICNC): multimed. Comput. And Commun., Santa Clara, CA, USA: 566-570.

  41. Zhou Y, Duan Y, Sun J, Guo Z (2014) Towards a simple and smooth rate adaption for VBR video in DASH. Proc. of the IEEE Vis. Commun. and Image Process. Conf, Valletta, pp 9–12.

    Book  Google Scholar 

  42. Zhou C, Lin C-W, Guo Z (2016) mDASH: a Markov decision-based rate adaptation approach for dynamic HTTP streaming. IEEE Trans. on Multimed 18(4):738–751.

    Article  Google Scholar 

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This work is supported by the PAID-10-18 Program of the Universitat Politècnica de València (Ayudas para contratos de acceso al sistema español de Ciencia, Tecnología e Innovación, en estructuras de investigación de la Universitat Politècnica de València) and by the Project 20180810 from the Universitat Politècnica de València (“Tecnologías de distribución y procesado de información multimedia y QoE”).

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Correspondence to Román Belda.

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Belda, R., de Fez, I., Arce, P. et al. Look ahead to improve QoE in DASH streaming. Multimed Tools Appl 79, 25143–25170 (2020).

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