[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120585-en":3,"doc-seo-120585-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120585,1099514068365,"Aurelia","https://ap-avatar.wpscdn.com/avatar/10000253d8d9f28188e?_k=1776742907772140068",6,"Technology","Application of Machine Learning for Managing High-Definition Video Streaming - 2021-2024综述","The paper investigates how machine learning can improve high-definition video streaming stability and quality. It reviews work from 2021 to 2024 and compares forecasting models, reinforcement learning, and selected unsupervised techniques for adaptive bitrate streaming. Coverage includes BiLSTM–CNN and GRU bandwidth/QoE prediction, reinforcement learning systems such as DQNReg, DeepVR, and GreenABR, plus clustering for QoE monitoring. Results indicate fewer stalls, smoother quality, and higher QoE versus legacy ABR rules, sometimes with reduced mobile energy use, while noting heavy runtime and limited generalization beyond training data. ","Application of machine learning for Managing HighDefinition Video Streaming  \nKumar Avinash*  \n*Software Development Engineer, Google ,Seattle, USA  \nAbstract  \nThe paper explores the idea of using machine learning for high-definition video streaming. Formats like 4K, 8K, and VR make this task harder because they need more stable delivery. The purpose is to review the work from 2021 to 2024 and apply different approaches — forecasting models, reinforcement learning, and some unsupervised methods—to see how they affect the stability and quality of adaptive bitrate streaming. The review covers a variety of research: forecasting with BiLSTM–CNN and GRU models, reinforcement learning systems like DQNReg, DeepVR, or GreenABR, and clustering techniques for monitoring QoE. The main results indicate that machine learning solutions outperform older ABR rules: fewer stalls, smoother quality, higher QoE, and in some cases lower power use on mobile devices. At the same time, the models are often heavy to run and may not generalize well outside the training data. The article is meant for researchers and engineers working on video delivery and network optimization, and points to where ML-based streaming could be applied in practice.  \nKeywords: machine learning; adaptive bitrate streaming; quality of experience; reinforcement learning; bandwidth prediction; unsupervised learning; video streaming; VR/360° video; energy-aware streaming; network optimization.  \n1. Introduction  \nOnline video has grown so quickly that it now dominates internet traffic. The web has effectively turned into a bandwidth-hungry space. High-definition, 4K, and even immersive formats like 360° and VR make the problem worse, since they demand far more stable delivery. Streaming services therefore run into constant challenges: networks fluctuate, devices differ, and users still expect smooth, high-quality playback without pauses. Traditional adaptive bitrate (ABR) algorithms try to handle this by switching video quality according to buffer size or throughput estimates. That approach works to a degree but is easy to break.  \nReceived: 9/19/2025  \nAccepted: 11/19/2025  \nPublished: 11/29/2025  \n* Corresponding author.  \nA sudden drop in bandwidth can trigger overreactions—long stalls, jarring quality changes, or wasted capacity when the network could have carried more.Machine learning has been proposed as a way out of this cycle. Predictive models, reinforcement learning, and even unsupervised methods make it possible to anticipate network changes instead of just reacting to them. Some of these methods aim at more than one target at once—they don’t just cut stalls, but also try to hold resolution steady or, on phones, save a bit of battery. Not every improvement comes from ML, though. Systems-level fixes can help too. For example, Prime Video’s Startover Playback, on which the author worked directly, tackled the problem of keeping live streams smooth on older or limited devices. Studies over the last few years show measurable gains in both objective terms (shorter stall times, fewer abrupt switches) and in subjective Quality of Experience (QoE) . ML opens up things the old ABR algorithms were notable to do. One clear case is virtual reality (VR) . In 360° video there is no necessity to send the entire scene in high resolution. Instead, the system can try to guess where the viewer will turn their head and keep that part sharp while lowering quality in the rest. That way, a lot of bandwidth is saved and most people will not notice the difference.  \nIn general, machine learning lets streaming players do things the old ABR rules could not handle. It makes it possible to forecast bandwidth, adjust bitrate before problems occur, and tune playback to the special demands ofimmersive formats. A practical example is viewport prediction in 360° video, where only the visible part of the scene is sent in high quality, saving bandwidth without lowering user satisfaction. These kinds of ","cbCaiixReuzeVKUv","https://ap.wps.com/l/cbCaiixReuzeVKUv","pdf",502869,1,10,"English","en",105,"# Introduction\n## Challenges of high-definition and immersive streaming\n## Role of machine learning vs traditional ABR\n# Methods and Materials\n## Clustering and supervised prediction approaches\n## Reinforcement learning and energy-aware methods\n## Bandwidth/QoE optimization for VR and wireless environments","[{\"question\":\"为什么高分辨率与沉浸式视频会让自适应码率（ABR）更难稳定？\",\"answer\":\"4K、8K 以及 360°/VR 等格式需要更稳定的交付，而网络波动、终端差异会导致缓冲不足、卡顿以及画质切换不平滑。传统 ABR 基于缓冲区或吞吐估计，容易在突发带宽下降时出现过度反应。\"},{\"question\":\"论文从哪些机器学习方向比较自适应码率流的稳定性与质量？\",\"answer\":\"研究覆盖 2021 到 2024 年的多类方法，包括带 BiLSTM–CNN 和 GRU 的预测模型、使用 DQNReg/DeepVR/GreenABR 等的强化学习方案，以及用于 QoE 监控的无监督聚类等方法。\"},{\"question\":\"机器学习在这类系统中的优势和局限分别是什么？\",\"answer\":\"优势主要体现在卡顿更少、画质更平滑、QoE 更高，且部分方案可降低移动端能耗。局限则包括模型运行开销较大，以及在训练数据之外的泛化能力可能不足。\"}]","Application of Machine Learning for Managing High-Definition Video Streaming - 2021-2024综述 | PDF",1785730770,25,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"application-of-machine-learning-for-managing-high-definition-video-streaming-2021-2024-review","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/application-of-machine-learning-for-managing-high-definition-video-streaming-2021-2024-review/120585/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"为什么高分辨率与沉浸式视频会让自适应码率（ABR）更难稳定？","Question",{"text":75,"@type":76},"4K、8K 以及 360°/VR 等格式需要更稳定的交付，而网络波动、终端差异会导致缓冲不足、卡顿以及画质切换不平滑。传统 ABR 基于缓冲区或吞吐估计，容易在突发带宽下降时出现过度反应。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"论文从哪些机器学习方向比较自适应码率流的稳定性与质量？",{"text":80,"@type":76},"研究覆盖 2021 到 2024 年的多类方法，包括带 BiLSTM–CNN 和 GRU 的预测模型、使用 DQNReg/DeepVR/GreenABR 等的强化学习方案，以及用于 QoE 监控的无监督聚类等方法。",{"name":82,"@type":73,"acceptedAnswer":83},"机器学习在这类系统中的优势和局限分别是什么？",{"text":84,"@type":76},"优势主要体现在卡顿更少、画质更平滑、QoE 更高，且部分方案可降低移动端能耗。局限则包括模型运行开销较大，以及在训练数据之外的泛化能力可能不足。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,113,118,123,128,131,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":114,"doc_module":4,"doc_module_name":46,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]