Deep learning-based computer-generated holography (CGH) has recently demonstrated tremendous potential in three-dimensional (3D) displays and yielded impressive display quality. However, current CGH techniques are mostly limited on generating and transmitting holograms with a resolution of 1080p, which is far from the ultra-high resolution (16K+) required for practical virtual reality (VR) and augmented reality (AR) applications to support a wide field o f v iew a nd l arge e ye b ox. O ne o f t he m ajor o bstacles i n c urrent C GH f rameworks lies in the limited memory available on consumer-grade GPUs which could not facilitate the generation of highdefinition h olograms. M oreover, t he e xisting h ologram c ompression r ate c an h ardly p ermit t he transmission of high-resolution holograms over a 5G communication network, which is crucial for mobile application. To overcome the aforementioned challenges, we proposed an efficient jo int fr amework fo r ho logram ge neration and transmission to drive the development of consumer-grade high-definition h olographic d isplays. S pecifically, for hologram generation, we proposed a plug-and-play module that includes a pixel shuffle la yer an d a lightweight holographic super-resolution network, enabling the current CGH networks to generate high-definition holograms. For hologram transmission, we presented an efficient ho lographic tr ansmission fr amework ba sed on foveated rendering. In simulations, we have successfully achieved the generation and transmission of holograms with a 4K resolution for the first t ime o n a n N VIDIA G eForce R TX 3 090 G PU. W e b elieve t he p roposed framework could be a viable approach for the evergrowing data issue in holographic displays.