Design And Implementation Of Video Streaming Client Based On IOS

The mobile Internet has entered the era leading by content consumption,various forms of media content are changing people's living habits and conditions.Because of its real-time,low barriers and high involvement,living video content has attracted a large number of users.To meet the needs of users,major Internet companies are strongly committed to live video business.They have proposed a lot of solutions for live video.However,these solutions usually do not fully open up their key technologies and can not provide comprehensive technical support for all developers who need to conduct live broadcast services.At the same time in some important technical aspects such as:portrait beautification process,for which the mainstream solution is usually based on bilateral filters to achieve.That may occupy much computing resources.In addition,when processing the video stream data,it is usually simply to process the video image frame by frame using an image algorithm ignoring interframe connection between the video images,thereby causing a possible flicker when playing video.In order to solve the problems above,we did the research on the design and implementation of live streaming client based on iOS platform.We first analyzed the technical nodes involved in the process of streaming and then proposed the realization plan based on the iOS platform.Secondly,we also realized the portrait beauty program which is involved in the process of streaming based on the guidance filtering,and combines the characteristics of the video stream to optimize the guidance filtering algorithm and improve the performance of the guidance filtering algorithm in the video stream application scenario.The optimization improved the stability of the processed video and reduced the flickering of the video.After the completion of the design and implementation of the client,we did a series of experience on the function and performance of the live streaming client,and results proved that the client could achieve all the functions involved in the streaming and proved that the optimization algorithm we proposed effectively reduced the occupation of computing resources and improved the quality of the live broadcast content.