The　massive　connectivity　trend　was　set　to　shape　B5G/6G　networks.　Device-to-device　(D2D)　communications　play　a　crucial　role　in　massive　connectivity　in　the　context　of　Internet　of　Things　(IoT)　applications.　Recently,　a　heterogeneous　interference　graph　neural　network　(HIGNN)　was　proposed　for　resource　allocation　in　heterogeneous　networks.　The　HIGNN　captured　the　spatial　information　hidden　in　heterogeneous　network　topology　and　was　scalable.　However,　existing　methods　primarily　focused　on　resource　allocation　at　the　physical　layer　only　and　did　not　adequately　address　the　cross-layer　issues　involved　in　video　transmission.　Therefore,　in　this　paper,　we　propose　the　video-optimized　heterogeneous　interference　graph　neural　network　(VD-HIGNN)　as　a　cross-layer　D2D　resource　allocation　method　for　video　transmission,　which　introduces　the　following　contributions:　1)　joint　source　encoder　rate　and　beamforming/power　control,　2)　incorporating　video　rate　distortion　function　parameters　from　the　application　layer　into　the　node　features,　and　3)　changing　the　loss　function　from　data　rate　to　Peak-Signal-to-Noise-Ratio　(PSNR),　a　function　of　video　rate　distortion　and　a　metric　of　video　quality.　Simulation　results　demonstrate　that　our　proposed　VD-HIGNN　outperforms　two　physical　layer　baseline　schemes:　the　iterative　fractional　programming　method　by　0.53　dB　and　HIGNN　by　approximately　2　dB　for　video　transmission.　Moreover,　when　scaled　to　larger　problems　with　2-12　times　the　number　of　nodes　within　a　fixed　area　size,　the　VD-HIGNN　achieves　94%　or　more　of　the　performance　of　a　retrained　model,　showcasing　its　scalability　and　generalization　ability.