The　development　of　the　Internet　of　Things　(IoT),　cyber　physical　systems,　and　ubiquitous　mobile　terminals　enable　video　content　to　be　shared　and　consumed　in　new　and　innovative　ways.　Nonetheless,　the　stochastic　and　unpredictable　nature　of　heterogeneous　wireless　networks　with　mobile　clients　presents　a　significant　challenge　to　the　increasing　demand　of　quality　of　experience　(QoE).　In　this　paper,　we　study　the　problem　of　maximizing　the　user＇s　QoE　of　viewing　streaming　video　via　automatic　bitrate　adaptation　in　heterogeneous　wireless　networks.　To　be　specific,　a　stochastic　optimization　problem　is　first　formulated　by　considering　fundamental　uncertainties　of　heterogeneous　wireless　networks　(i.e.,　the　stochastic　throughput).　A　dynamic　bitrate　adaptation　scheme　is　then　designed　based　on　the　Lyapunov　optimization　framework.　Our　proposed　scheme　conducts　video　bitrate　selection　based　on　the　current　queue　buffer　state　and　real-time　throughput,　and　is　capable　of　balancing　the　tradeoff　between　a　user＇s　QoE　and　buffer　occupation　(i.e.,　memory　utilization).　The　performance　of　our　proposed　scheme　is　investigated　through　a　combination　of　extensive　analysis,　simulations,　and　experiments　in　a　real-world　testbed.　Simulation　results　demonstrate　that　our　scheme　outperforms　the　baseline　with　regard　to　QoE　and　bandwidth　utilization.　In　addition,　the　experimental　results　in　real-world　testbed　validate　the　scheme＇s　efficiency　and　practicality　in　real-world　system.