Improving DASH Encoding with Scenes and Downscaling Techniques for VoD Streaming

Video content represents a high percentage of the traffic on Internet with an increasing number of platforms offering live and on demand video. Most platforms use HTTP Adaptive Streaming (HAS) to deliver their content. In HAS, the provider splits the videos in segments and each segment is offered in multiple representations. The player can dynamically request the appropriate representation for the next segments depending on the varying conditions. This alleviates problems such as initial delay or stalling, thus providing a better quality of experience to users. These segments can have a fixed duration or can be adapted to match scenes up to a maximum duration of typically 10 seconds. In this work, we study the effects in time, quality and size of the encoded videos when downscaling the video to obtain the scenes. The experiments are performed using two codecs: H.264 and VP9, and using 10 videos with 4K resolution with a duration of more than 150 seconds. The videos are encoded using fixed segments and variable segments based on scenes (obtained from the downscaled video). Results show that the use of downscaling to obtain the scenes has a small impact on the final quality, while reduces the total time, the consumption of computational resources, and the size of the encoded video.