Edge　computing　is　conducive　to　reducing　service　response　time　and　improving　service　quality　by　pushing　cloud　functions　to　a　network＇s　edges.　Most　existing　works　in　edge　computing　focus　on　utility　maximization　of　task　offloading　on　static　edges　with　a　single　antenna.　Besides,　trajectory　planning　of　mobile　edges,　e.g.,　autonomous　aerial　vehicles　(AAVs)　is　also　rarely　discussed.　In　this　paper,　we　are　the　first　to　jointly　discuss　the　deadline-ware　task　offloading　and　AAV　trajectory　planning　problem　in　a　multi-input　multi-output　(MIMO)　AAV-aided　mobile　edge　computing　system.　Due　to　discrete　variables　and　highly　coupling　nonconvex　constraints,　we　equivalently　convert　the　original　problem　into　a　more　solvable　form　by　introducing　auxiliary　variables.　Next,　a　penalty　dual　decomposition-based　algorithm　is　developed　to　achieve　a　global　optimal　solution　to　the　problem.　Besides,　we　proposed　a　profit-based　fireworks　algorithm　in　a　relatively　lower　time　to　reduce　the　execution　time　for　large-scale　networks.　Extensive　evaluation　results　reveal　that　our　proposed　optimal　algorithms　could　significantly　outperform　static　offloading　algorithms　and　other　algorithms　by　25%　on　average.