Short video is a new form of video that is shared on online social platforms based on user-generated content. However, it is worth noting that short video companies spend a lot on bandwidth. Saving bandwidth overhead without reducing user quality of experience (QoE) has become an important issue. In short video applications, users watch videos in the mode of sliding and watching. To ensure user QoE, the current video and videos in the recommendation queue need to be preloaded. However, if the user slides away, the downloaded but unwatched data does not contribute to improving user QoE, which results in wasted bandwidth.
The challenge of reducing the bandwidth wastage is to match the video download mechanism with user viewing behavior and network conditions. This problem is challenging as bandwidth savings conflict with user QoE improvement. Firstly, the download data volume for a certain video is difficult to determine, as user viewing behavior is unknown and difficult to model. Moreover, the user's viewing behavior varies greatly, with many factors influencing it such as content and viewing history. Secondly, conflicts exist between the downloads of different videos and the download sequence of videos is difficult to decide. Finally, selecting the right bitrate can be very challenging, because the network changes dynamically and it is hard to predict.
We will provide a simulator platform, a set of video traces, a set of network traces, and a set of common evaluation metrics, which the challenge participants can use to implement and evaluate their algorithms. We hope that the platform and dataset will serve as a common tool for researchers to benchmark their algorithms with each other and thus contribute towards reproducible research.
For this grand challenge, we consider the following scenario for short video applications. Users generate content and upload the videos, which are then processed and uploaded to the content delivery network (CDN) nodes. At the client side, in addition to the currently playing videos, some other videos are placed in the recommendation queue. So, users can watch videos in the mode of sliding and watching, that is, the user can slide away while watching the current video and continue to watch the next video.
To ensure user QoE, the current video is pre-buffered and videos in the recommendation queue need to be preloaded. However, if the user slides away, the downloaded but unwatched data are wasted.
The main task for this grand challenge is to design the algorithm which decides the download video chunk and its bitrate, in addition to that, to reduce bandwidth waste, the pause time should also be determined.
1. Decide which chunk to download next and its bitrate. According to the network condition and the buffer of videos to determine which chunk should be downloaded next and its bitrate, with the consideration of user QoE.
2. Decide the pause time during which the download process stops. To reduce the bandwidth waste, the download may be stopped if the network condition is good when taking bandwidth waste into account.