The　available　bandwidth　of　5G　networks　is　a　pivotal　factor　influencing　real-time　video　services.　However,　how　to　achieve　accurate　predictions　in　the　context　of　low-latency　real-time　video　services　remains　a　difficult　problem.　Conventional　algorithms　for　predicting　available　bandwidth　typically　rely　on　data　metrics　at　the　application　layer　and　complete　the　forecast　according　to　the　packet　transmission　strategies.　Such　algorithms　can　lead　to　prediction　lag　in　complex　scenarios,　thereby　significantly　impairing　the　received　video　quality　for　users.　To　address　this　concern,　this　study　proposed　a　novel　available　bandwidth　prediction　algorithm　based　on　cross-layer　multi-dimensional　parameters.　This　algorithm　comprehensively　integrates　pertinent　data　metrics　from　the　application,　physical,　and　network　layers,　utilizes　multiple　dimensions　of　parameters　to　enhance　the　precision　of　wireless　network　bandwidth　detection.　In　this　paper,　deep　reinforcement　learning　was　adopted　as　the　model　framework　to　integrate　offline　prediction　and　online　prediction　through　cross-layer　and　multi-dimensional　data　model　learning　for　different　motion　scenarios.　Furthermore,　network　packet　loss　rates,　image　quality　assessments,　end-to-end　delays,　and　other　link-related　factors　were　introduced　as　constraints,　to　realize　the　real-time　adjustment　and　optimization　of　the　prediction　model　during　the　transmission　process.　Experimental　results　conducted　on　a　semi-physical　platform　show　that:　the　prediction　performance　of　the　proposed　algorithm　is　better　than　the　traditional　prediction　method,　and　the　fitting　degree　of　the　prediction　curve　and　the　actual　curve　is　more　than　95.　8%;　compared　with　the　single-layer　prediction　algorithm,　the　packet　loss　rate　of　the　proposed　algorithm　in　walking　and　driving　scenarios　decreases　by　47.　3%　and　30.　9%,　respectively,　and　the　quality　of　received　video　is　improved　by　12%.　©　2023　South　China　University　of　Technology.　All　rights　reserved.