Preoperative assessment and optimisation: The key to good outcomes after the pandemic

Complications following surgery are common, predictable and often preventable. New preoperative assessment and optimisation guidance recommends clear pathways with triggers for interventions, patient involvement, shared decision making and team education, to help both patients and service efficiency

HUBUNGAN MASA KERJA DI LUAR RUANGAN DAN PENGGUNAAN APD DENGAN KEJADIAN KATARAK PASIEN POLI MATA RSUD S. K. LERIK KUPANG

Katarak berada di posisi kedua penyebab gangguan penglihatan terbanyak di seluruh dunia (33%) setelah gangguan refraksi dan di urutan pertama penyebab kebutaan terbanyak di dunia (51%).Radiasi UV dan stres oksidatif dianggap sebagai faktor penting dalam patogenesis katarak. Orang yang bekerja lebih banyak di luar gedung berisiko mengalami katarak, terlebih bagi mereka yang tidak menggunakan alat pelindung diri (APD) untuk mata. Tujuan penelitian ini untuk mengetahui hubungan masa kerja di luar ruangan dan penggunaan APD dengan kejadian katarak pasien Poli Mata RSUD S. K. Lerik Kupang 2018-2019. Metode  penelitian ini adalah analitik observasional dengan desain kasus kontrol. Populasi dalam penelitian ini adalah semua pasienpolimata RSUD S. K. LerikKupang. Sampel dalam penelitian berjumlah 60 responden dengan teknik sampel yang digunakan adalah purposive  sampling. Pengumpulan data dilakukan dengan kuesioner. Data dianalisis dengan rumus uji koefisien kontingensi c. Hasil analsisi menunjukkan tidak derdapat hubungan yang bermakna antara masa kerja di luar ruangan dengan kejadian katarak,p value = 0,640 (p > 0,05) dengan OR=1,556, dan tidak derdapat hubungan yang bermakna antara penggunaan APD dengan kejadian katarak,p value = 0,129 dan OR=0,289 (<1). Kesimpulan dari penelitian ini tidak terdapat hubungan yang bermakna antara masa kerja di luar ruangan dan penggunaan APD dengan terjadinya katarak di Poli Mata RSUD. S. K. Lerik Kupan

Timing of elective surgery and risk assessment after SARS-CoV-2 infection: 2023 update

Guidance for the timing of surgery following SARS-CoV-2 infection needed reassessment given widespread vaccination, less virulent variants, contemporary evidence and a need to increase access to safe surgery. We, therefore, updated previous recommendations to assist policymakers, administrative staff, clinicians and, most importantly, patients. Patients who develop symptoms of SARS-CoV-2 infection within 7 weeks of planned surgery, including on the day of surgery, should be screened for SARS-CoV-2. Elective surgery should not usually be undertaken within 2 weeks of diagnosis of SARS-CoV-2 infection. For patients who have recovered from SARS-CoV-2 infection and who are low risk or having low-risk surgery, most elective surgery can proceed 2 weeks following a SARS-CoV-2 positive test. For patients who are not low risk or having anything other than low-risk surgery between 2 and 7 weeks following infection, an individual risk assessment must be performed. This should consider: patient factors (age; comorbid and functional status); infection factors (severity; ongoing symptoms; vaccination); and surgical factors (clinical priority; risk of disease progression; grade of surgery). This assessment should include the use of an objective and validated risk prediction tool and shared decision-making, taking into account the patient's own attitude to risk. In most circumstances, surgery should proceed unless risk assessment indicates that the risk of proceeding exceeds the risk of delay. There is currently no evidence to support delaying surgery beyond 7 weeks for patients who have fully recovered from or have had mild SARS-CoV-2 infection

Success rate of prehospital emergency front-of-neck access (FONA): a systematic review and meta-analysis

Background Front-of-neck access (FONA) is an emergency procedure used as a last resort to achieve a patent airway in the prehospital environment. In this systematic review with meta-analysis, we aimed to evaluate the number and success rate of FONA procedures in the prehospital setting, including changes since 2017, when a surgical technique was outlined as the first-line prehospital method. Methods A systematic literature search (PROSPERO CRD42022348975) was performed from inception of databases to July 2022 to identify studies in patients of any age undergoing prehospital FONA, followed by data extraction. Meta-analysis was used to derive pooled success rates. Methodological quality of included studies was interpreted using the Cochrane risk of bias tool, and rated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach. Results From 909 studies, 69 studies were included (33 low quality; 36 very low quality) with 3292 prehospital FONA attempts described (1229 available for analysis). The crude median success rate increased from 99.2% before 2017 to 100.0% after 2017. Meta-analysis revealed a pooled overall FONA success rate of 88.0% (95% confidence interval [CI], 85.0–91.0%). Surgical techniques had the highest success rate at a median of 100.0% (pooled rate=92.0%; 95% CI, 88.0–95.0%) vs 50.0% for needle techniques (pooled rate=52.0%; 95% CI, 28.0–76.0%). Conclusions Despite being a relatively rare procedure in the prehospital setting, the success rate for FONA is high. A surgical technique for FONA appears more successful than needle techniques, and supports existing UK prehospital guidelines

healthcareCOVID: a national cross-sectional observational study identifying risk factors for developing suspected or confirmed COVID-19 in UK healthcare workers.

To establish the prevalence, risk factors and implications of suspected or confirmed coronavirus disease 2019 (COVID-19) infection among healthcare workers in the United Kingdom (UK). Cross-sectional observational study. UK-based primary and secondary care. Healthcare workers aged ≥18 years working between 1 February and 25 May 2020. A composite endpoint of laboratory-confirmed diagnosis of SARS-CoV-2, or self-isolation or hospitalisation due to suspected or confirmed COVID-19. Of 6,152 eligible responses, the composite endpoint was present in 1,806 (29.4%) healthcare workers, of whom 49 (0.8%) were hospitalised, 459 (7.5%) tested positive for SARS-CoV-2, and 1,776 (28.9%) reported self-isolation. Overall, between 11,870 and 21,158 days of self-isolation were required by the cohort, equalling approximately 71 to 127 working days lost per 1,000 working days. The strongest risk factor associated with the presence of the primary composite endpoint was increasing frequency of contact with suspected or confirmed COVID-19 cases without adequate personal protective equipment (PPE): 'Never' (reference), 'Rarely' (adjusted odds ratio 1.06, (95% confidence interval: [0.87-1.29])), 'Sometimes' (1.7 [1.37-2.10]), 'Often' (1.84 [1.28-2.63]), 'Always' (2.93, [1.75-5.06]). Additionally, several comorbidities (cancer, respiratory disease, and obesity); working in a 'doctors' role; using public transportation for work; regular contact with suspected or confirmed COVID-19 patients; and lack of PPE were also associated with the presence of the primary endpoint. A total of 1,382 (22.5%) healthcare workers reported lacking access to PPE items while having clinical contact with suspected or confirmed COVID-19 cases. Suspected or confirmed COVID-19 was more common in healthcare workers than in the general population and is associated with significant workforce implications. Risk factors included inadequate PPE, which was reported by nearly a quarter of healthcare workers. Governments and policymakers must ensure adequate PPE is available as well as developing strategies to mitigate risk for high-risk healthcare workers during future COVID-19 waves. [Abstract copyright: © 2021 Kua et al.

Timing of elective surgery and risk assessment after SARS‐CoV ‐2 infection:an update: A multidisciplinary consensus statement on behalf of the Association of Anaesthetists, Centre for Perioperative Care, Federation of Surgical Specialty Associations, Royal College of Anaesthetists, Royal College of Surgeons of England

The impact of vaccination and new SARS‐CoV‐2 variants on peri‐operative outcomes is unclear. We aimed to update previously published consensus recommendations on timing of elective surgery after SARS‐CoV‐2 infection to assist policymakers, administrative staff, clinicians and patients. The guidance remains that patients should avoid elective surgery within 7 weeks of infection, unless the benefits of doing so exceed the risk of waiting. We recommend individualised multidisciplinary risk assessment for patients requiring elective surgery within 7 weeks of SARS‐CoV‐2 infection. This should include baseline mortality risk calculation and assessment of risk modifiers (patient factors; SARS‐CoV‐2 infection; surgical factors). Asymptomatic SARS‐CoV‐2 infection with previous variants increased peri‐operative mortality risk three‐fold throughout the 6 weeks after infection, and assumptions that asymptomatic or mildly symptomatic omicron SARS‐CoV‐2 infection does not add risk are currently unfounded. Patients with persistent symptoms and those with moderate‐to‐severe COVID‐19 may require a longer delay than 7 weeks. Elective surgery should not take place within 10 days of diagnosis of SARS‐CoV‐2 infection, predominantly because the patient may be infectious, which is a risk to surgical pathways, staff and other patients. We now emphasise that timing of surgery should include the assessment of baseline and increased risk, optimising vaccination and functional status, and shared decision‐making. While these recommendations focus on the omicron variant and current evidence, the principles may also be of relevance to future variants. As further data emerge, these recommendations may be revised

Timing of elective surgery and risk assessment after SARS-CoV-2 infection: an update: A multidisciplinary consensus statement on behalf of the Association of Anaesthetists, Centre for Perioperative Care, Federation of Surgical Specialty Associations, Royal College of Anaesthetists, Royal College of Surgeons of England

The impact of vaccination and new SARS-CoV-2 variants on peri-operative outcomes is unclear. We aimed to update previously published consensus recommendations on timing of elective surgery after SARS-CoV-2 infection to assist policymakers, administrative staff, clinicians and patients. The guidance remains that patients should avoid elective surgery within 7 weeks of infection, unless the benefits of doing so exceed the risk of waiting. We recommend individualised multidisciplinary risk assessment for patients requiring elective surgery within 7 weeks of SARS-CoV-2 infection. This should include baseline mortality risk calculation and assessment of risk modifiers (patient factors; SARS-CoV-2 infection; surgical factors). Asymptomatic SARS-CoV-2 infection with previous variants increased peri-operative mortality risk three-fold throughout the 6 weeks after infection, and assumptions that asymptomatic or mildly symptomatic omicron SARS-CoV-2 infection does not add risk are currently unfounded. Patients with persistent symptoms and those with moderate-to-severe COVID-19 may require a longer delay than 7 weeks. Elective surgery should not take place within 10 days of diagnosis of SARS-CoV-2 infection, predominantly because the patient may be infectious, which is a risk to surgical pathways, staff and other patients. We now emphasise that timing of surgery should include the assessment of baseline and increased risk, optimising vaccination and functional status, and shared decision-making. While these recommendations focus on the omicron variant and current evidence, the principles may also be of relevance to future variants. As further data emerge, these recommendations may be revised

SARS-CoV-2 infection, COVID-19 and timing of elective surgery: A multidisciplinary consensus statement on behalf of the Association of Anaesthetists, the Centre for Peri-operative Care, the Federation of Surgical Specialty Associations, the Royal College of Anaesthetists and the Royal College of Surgeons of England

The scale of the COVID-19 pandemic means that a significant number of patients who have previously been infected with SARS-CoV-2 will require surgery. Given the potential for multisystem involvement, timing of surgery needs to be carefully considered to plan for safe surgery. This consensus statement uses evidence from a systematic review and expert opinion to highlight key principles in the timing of surgery. Shared decision-making regarding timing of surgery after SARS-CoV-2 infection must account for severity of the initial infection; ongoing symptoms of COVID-19; comorbid and functional status; clinical priority and risk of disease progression; and complexity of surgery. For the protection of staff, other patients and the public, planned surgery should not be considered during the period that a patient may be infectious. Precautions should be undertaken to prevent pre- and peri-operative infection, especially in higher risk patients. Elective surgery should not be scheduled within 7 weeks of a diagnosis of SARS-CoV-2 infection unless the risks of deferring surgery outweigh the risk of postoperative morbidity or mortality associated with COVID-19. SARS-CoV-2 causes either transient or asymptomatic disease for most patients, who require no additional precautions beyond a 7-week delay, but those who have persistent symptoms or have been hospitalised require special attention. Patients with persistent symptoms of COVID-19 are at increased risk of postoperative morbidity and mortality even after 7 weeks. The time before surgery should be used for functional assessment, prehabilitation and multidisciplinary optimisation. Vaccination several weeks before surgery will reduce risk to patients and might lessen the risk of nosocomial SARS-CoV-2 infection of other patients and staff. National vaccine committees should consider whether such patients can be prioritised for vaccination. As further data emerge, these recommendations may need to be revised, but the principles presented should be considered to ensure safety of patients, the public and staff

Next-to-Leading Order Constituent Quark Structure and Hadronic Structure Functions

We calculate the partonic structure of a constituent quark in the Next-to-Leading Order framework. The structure of any hadron can be obtained thereafter using a convolution method. Such a procedure is used to generate the structure function of proton and pion in NLO, neglecting certain corrections to $\Lambda_{QCD}$. It is shown that while the constituent quark structure is generated purely perturbatively and accounts for the most part of the hadronic structure, there is a few percent contributions coming from the nonperturbative sector in the hadronic structure. This contribution plays the key role in explaining the SU(2) symmetry breaking of the nucleon sea and the observed violation of Gottfried sum rule. These effects are calculated. We obtained an Excellent agreement with the experimental data in a wide range of $x=[10^{-6}, 1]$ and $Q^{2}=[0.5, 5000]$ $GeV^{2}$ for the proton structure function. We have also calculated Pion structure and compared it with the existing data. Again, the model calculations agree rather well with the data from experiment.Comment: 32 pages,10 figures, Accepted to publish in Phys. Rev.

Look ahead to improve QoE in DASH streaming

[EN] 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.This work is supported by the PAID-10-18 Program of the Universitat Politecnica de Valencia (Ayudas para contratos de acceso al sistema espanol de Ciencia, Tecnologia e Innovacion, en estructuras de investigacion de la Universitat Politecnica de Valencia) and by the Project 20180810 from the Universitat Politecnica de Valencia ("Tecnologias de distribucion y procesado de informacion multimedia y QoE").Belda Ortega, R.; De Fez Lava, I.; Arce Vila, P.; Guerri Cebollada, JC. (2020). Look ahead to improve QoE in DASH streaming. Multimedia Tools and Applications. 79(33-34):25143-25170. https://doi.org/10.1007/s11042-020-09214-9S25143251707933-34Akhshabi 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. https://doi.org/10.1016/j.image.2011.10.003Android Developers webpage, ExoPlayer. Available online at: https://developer.android.com/guide/topics/media/exoplayer.html . Accessed: Jun. (2019)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. https://doi.org/10.1109/TCSVT.2018.2868262Barman N, Martini MG (2019) QoE modeling for HTTP adaptive video streaming - a survey and open challenges. IEEE Access 7:30831–30859. https://doi.org/10.1109/ACCESS.2019.2901778Belda R (2013) Algoritmo de adaptación DASH: Look Ahead. Master Thesis. Universitat Politècnica de València. http://hdl.handle.net/10251/33359 .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. https://doi.org/10.1109/BMSB.2018.8436718 .Blender Foundation webpage. Available online at: https://www.blender.org/foundation . Accessed: Jun. (2019).Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20-3:273–297. https://doi.org/10.1023/A:1022627411411DASH Industry forum webpage. Available online at: http://dashif.org . Accessed: Jun. (2019)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. https://doi.org/10.1109/TCSVT.2017.2768542Ghent University. 4G/LTE bandwidth logs. Available online at: http://users.ugent.be/~jvdrhoof/dataset-4g . Accessed: Jun. (2019).Github webpage. A DASH segment size aware rate adaptation model for DASH. Available online at: https://github.com/pari685/AStream . Accessed: Jun. (2019)GitHub website. Dashgen, Multimedia Communications Group. Available online at: https://github.com/comm-iteam/dashgen . Accessed: Jun. (2019).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. https://doi.org/10.1109/LCOMM.2016.2601087Huang 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. https://doi.org/10.1145/2619239.2626296Institute of Telecommunications and Multimedia Applications website. Look Ahead Demo. Available online at: https://lookahead.iteam.upv.es . Accessed: Jun. (2019)ISO/IEC 23009–1:2014 (2014) Dynamic adaptive streaming over HTTP (DASH) - Part 1: media presentation description and segment formats.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. https://doi.org/10.1109/ICCW.2015.7247436 .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. https://doi.org/10.1109/NOMS.2016.7502805 .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. https://doi.org/10.1109/COMST.2017.2685630Lee 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. https://doi.org/10.1109/ICCE.2015.7066348 .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. https://doi.org/10.1109/TMM.2011.2152382Liu 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. https://doi.org/10.1145/1943552.1943575 .Medium webpage (2016) Toward a practical perceptual video quality metric. Available online at: https://medium.com/netflix-techblog/toward-a-practical-perceptual-video-quality-metric-653f208b9652 . Accessed: Jun. 2019.Mobile Video Service Performance Study (2015) HUAWEI white paper. Available online at: http://www.ctiforum.com/uploadfile/2015/0701/20150701091255294.pdf .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. https://doi.org/10.1145/2155555.2155558Moldovan 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. https://doi.org/10.23919/ITC.2017.8065802Müller 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. https://doi.org/10.1145/2151677.2151686Nguyen 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. https://doi.org/10.1109/ComManTel.2015.7394285 .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. https://doi.org/10.1145/3281411.3281439 .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. https://doi.org/10.1109/TMM.2017.2733340Sheikh H, Bovik A (2006) Image information and visual quality. IEEE Trans on Image Process 15(2):430–444. https://doi.org/10.1109/TIP.2005.859378Shuai 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. https://doi.org/10.1109/ICCE-Berlin.2016.7684742 .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. https://doi.org/10.1109/JSAC.2016.2577361Yarnagula 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 https://doi.org/10.1145/3311749 .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. https://doi.org/10.1145/2670518.2673877 .YouTube webpage (2019) Youtube press. Available online at: https://www.youtube.com/yt/about/press . Accessed: Jun. 2019.Youtube webpage, Google I/O ‘18: Building feature-rich media apps with ExoPlayer. Available online at: https://youtu.be/svdq1BWl4r8?t=2m . Published: May (2018)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. https://doi.org/10.1109/TBC.2017.2687698Zhao 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. https://doi.org/10.1109/ICCNC.2017.7876191 .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. https://doi.org/10.1109/VCIP.2014.7051491Zhou 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. https://doi.org/10.1109/TMM.2016.252265