With　the　rapid　development　of　mobile　communication　technology,　short　video　applications,　which　combine　the　features　of　both　social　and　multimedia　applications,　have　become　more　and　more　popular.　However,　the　transmission　of　short　videos　poses　great　challenges　to　the　existing　mobile　networks.　In　this　paper,　mobile　edge　computing　is　adopted　to　provide　content　caching　of　short　videos　close　to　end　users.　To　improve　the　quality　of　service,　we　take　both　the　quality　level　of　the　video　and　the　long-term　wireless　network　transmission　performance　into　consideration.　The　joint　video　quality　selection　and　radio　bearer　control　optimization　problem　is　formulated　as　a　Markov　decision　process,　aiming　at　maximizing　the　long-term　video　quality　profit　and　minimizing　the　cost　of　bearers　and　the　penalty　of　latency.　Deep　reinforcement　learning　is　used　as　the　solution　and　the　policy　gradient　based　quality　selection　and　radio　bearer　control　method　is　proposed.　The　REINFORCE　algorithm　with　baseline　is　used　to　train　the　policy　network,　and　the　episodic　simulations　are　built　to　obtain　the　training　samples.　Different　weight　coefficients　of　the　objective　function　are　configured.　Training　results　show　that　the　proposed　method　can　achieve　the　best　accumulated　value　among　all　the　comparison　methods.　When　the　weight　coefficients　are　changed,　the　training　processes　can　lead　the　policy　networks　to　obtain　proper　trade-off　between　different　objective　factors.　Moreover,　the　performance　of　the　trained　policy　network　is　evaluated　with　different　short　video　request　arriving　rates.　Testing　results　show　that　the　proposed　method　performs　well　when　the　arriving　rates　vary　in　a　certain　range.