The　key　for　3D　video　network　service　is　to　improve　the　quality　of　experience　(QoE)　of　users,　which　can　be,　however,　affected　by　mutable　network　conditions　and　video　contents.　For　conventional　2D　videos,　the　HTTP　adaptive　streaming　(HAS)　technique　has　demonstrated　its　significance　in　improving　user　QoE　by　utilizing　dynamically　switched　bitrates,　while　for　3D　video　transmission　with　at　least　two　video　streams,　this　technique　has　not　yet　been　extensively　explored.　Dynamic　conversion　policy　of　the　video　quality　level　is　the　core　of　HAS　technique.　In　this　work,　we　investigate　the　impact　on　user　QoE　when　introducing　dynamic　bitrates　to　different　3D　videos.　A　subjective　database　is　built　to　illustrate　the　connection　between　block-level　objective　quality,　which　changes　with　bitrates,　and　the　QoE　of　3D　vision.　Through　this,　we　propose　a　convolutional　neural　network　(CNN)　based　QoE　model　that　effectively　assesses　the　QoE　by　block-level　objective　quality.　The　Pearson　linear　correlation　coefficient　(PLCC)　of　the　model　predictive　value　and　the　mean　opinion　score　(MOS)　is　0.906.The　proposed　framework　can　provide　guidance　to　inter-view　bitrate　balancing　of　HAS　for　3D　video　transmission.　©　2019,　Editorial　Board　of　JBUAA.　All　right　reserved.