MamBRA: Session-Level Bandwidth Prediction for Adaptive Video Streaming using Selective State Space Models

MamBRA: Session-Level Bandwidth Prediction for Adaptive Video Streaming using Selective State Space Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article MamBRA: Session-Level Bandwidth Prediction for Adaptive Video Streaming using Selective State Space Models Jamal Hussein, Aree Mohammed, Miran Abdullah This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9024915/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Live streaming is the real-time transmission of video content to an audience as it is simultaneously recorded. This technology is frequently utilized for applications such as covering live events and facilitating video calls. By dynamically modifying the video quality to match network conditions and device capabilities, adaptive video streaming provides improved Quality of Experience (QoE). However, as user demands for high quality and low latency increase, using efficient video streaming systems is getting harder. In addition to taxing network resources, the increase in video traffic is lowering video quality. Deep and transformer learning algorithms use data-driven methods to optimize video delivery, enhance QoE, and lessen network congestion in order to overcome these obstacles. Mamba utilizes the efficient linear complexity of selective state space model (SSM) mechanism to process data sequences more effectively. This paper proposes an adaptive video streaming framework (MamBRA) based on Mamba for session-level bandwidth prediction. The model is trained in a supervised time-series manner on disjoint user sessions to prevent information leakage and preserve temporal structure. During inference, it leverages the linear state-space formulation of Mamba to efficiently generate stable bandwidth predictions within each session. Experimental results demonstrate reduced prediction error, improved accuracy, and enhanced temporal stability. The model achieves an overall inference accuracy of 93.94%, with session-level accuracy reaching as high as 97.32%. Furthermore, the predicted bandwidth achieves more consistent QoE scores compared to the PPO-based approach used in Pensieve. SSM-Mamba bitrate adaptation video streaming network bandwidth QoE Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 04 May, 2026 Editor assigned by journal 24 Mar, 2026 Submission checks completed at journal 06 Mar, 2026 First submitted to journal 03 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9024915","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637352191,"identity":"aac7a687-bc1f-4875-becb-a50d352a590a","order_by":0,"name":"Jamal Hussein","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACxgYg8YCBQYaNHchKAHL4gFgCjPBpAarkYeM5ANHCRkgLGIC0MEgkQDhQLbgBc/vpxA8JDHY8fJJvzB48bKtLbGNgPnibh8FCtgGXw3pyNwPNT+Zhk84xN0hsOwzUwpZsDbTUGKeWhtwNQC3MIC1mEglnDgC18JhJA7Uk4tTS/3bzjwSGeh42yTMgLSCH8X/Dr2VG7jagLYd52CR4gFoqmEG2sBHQ8nabRYLBcWAgp5UBtRw2bmNmM7acY4DbL4b9uZtvfKiolpNvP7xN8odBnWw/e/PDG28q6nCGmCFYwgBZiBkiwohLizwOcQbcWkbBKBgFo2DEAQCfBUl2AcBy4gAAAABJRU5ErkJggg==","orcid":"","institution":"University of Sulaymaniyah","correspondingAuthor":true,"prefix":"","firstName":"Jamal","middleName":"","lastName":"Hussein","suffix":""},{"id":637352192,"identity":"05a0081c-7d72-4820-a953-d6cb9bf36c4f","order_by":1,"name":"Aree Mohammed","email":"","orcid":"","institution":"University of Sulaymaniyah","correspondingAuthor":false,"prefix":"","firstName":"Aree","middleName":"","lastName":"Mohammed","suffix":""},{"id":637352193,"identity":"6627da57-f21b-4338-8a2b-0e354c7eb631","order_by":2,"name":"Miran Abdullah","email":"","orcid":"","institution":"University of Sulaymaniyah","correspondingAuthor":false,"prefix":"","firstName":"Miran","middleName":"","lastName":"Abdullah","suffix":""}],"badges":[],"createdAt":"2026-03-04 02:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9024915/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9024915/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109063142,"identity":"bc6446e1-c2f7-48d8-932d-a186b15165b2","added_by":"auto","created_at":"2026-05-12 08:45:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3932298,"visible":true,"origin":"","legend":"","description":"","filename":"mambra2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9024915/v1_covered_4254418a-5dc6-4da1-be54-5ccdcf966b39.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MamBRA: Session-Level Bandwidth Prediction for Adaptive Video Streaming using Selective State Space Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"SSM-Mamba, bitrate adaptation, video streaming, network bandwidth, QoE","lastPublishedDoi":"10.21203/rs.3.rs-9024915/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9024915/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Live streaming is the real-time transmission of video content to an audience as it is simultaneously recorded. 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