Currently,　an　effective　stream　adaptation　method　for　stereo　panoramic　video　transmission　is　missing.　However,　the　traditional　panoramic　video　adaptive　streaming　strategy　for　transmitting　binocular　stereo　panoramic　video　suffers　from　the　problem　of　doubling　the　transmission　data　and　requiring　huge　bandwidth.　A　multi-agent　reinforcement　learning　based　stereo　panoramic　video　asymmetric　transmission　adaptive　streaming　method　is　proposed　in　this　paper　to　cope　with　the　limited　bandwidth　and　fluctuation　of　network　bandwidth　in　real　time.　First,　due　to　the　human　eye＇s　preference　for　the　saliency　regions　of　video,　each　tile　in　the　left　and　right　viewpoints　of　stereoscopic　video　contributes　differently　to　the　perceptual　quality,　and　a　tiles-based　method　for　predicting　the　watching　probability　of　left　and　right　viewpoint　is　proposed.　Second,　a　multi-agent　reinforcement　learning　framework　based　on　policy-value　(Actor-Critic)　is　designed　for　joint　rate　control　of　left　and　right　viewpoints.　Finally,　a　reasonable　reward　function　is　designed　based　on　the　model　structure　and　the　principle　of　binocular　suppression.　The　experimental　results　show　that　the　proposed　method　is　more　suitable　for　tiles-based　stereo　panoramic　video　transmission　than　the　traditional　self-adaptive　stream　transmission　strategy.　A　novel　approach　is　proposed　for　stereo　panoramic　video　joint　rate　control　and　user　Quality　of　Experience　(QoE)　improvement　under　limited　bandwidth.　©　2022,　Science　Press.　All　right　reserved.