Video streaming often requires transcoding content into different resolutions
and bitrates to match the recipient's internet speed and screen capabilities.
Video encoders like x264 offer various presets, each with different tradeoffs
between transcoding time and rate-distortion performance. Choosing the best
preset for video transcoding is difficult, especially for live streaming, as
trying all the presets and choosing the best one is not feasible. One solution
is to predict each preset's transcoding time and select the preset that ensures
the highest quality while adhering to live streaming time constraints.
Prediction of video transcoding time is also critical in minimizing streaming
delays, deploying resource management algorithms, and load balancing. We
propose a learning-based framework for predicting the transcoding time of
videos across various presets. Our predictor's features for video transcoding
time prediction are derived directly from the ingested stream, primarily from
the header or metadata. As a result, only minimal additional delay is incurred
for feature extraction, rendering our approach ideal for live-streaming
applications. We evaluated our learning-based transcoding time prediction using
a dataset of videos. The results demonstrate that our framework can accurately
predict the transcoding time for different presets, with a mean absolute
percentage error (MAPE) of nearly 5.0%. Leveraging these predictions, we then
select the most suitable transcoding preset for live video streaming. Utilizing
our transcoding time prediction-based preset selection improved Peak
Signal-to-Noise Ratio (PSNR) of up to 5 dB.

我们提出了一种基于学习的框架，用于预测视频跨不同预设的转码时间，并通过选择最合适的转码预设来改善实时视频流的质量。

通过转码时间预测和预选，实现高质量的实时视频流传输

High-Quality Live Video Streaming via Transcoding Time Prediction and  Preset Selection

Despite gaining traction in North America, live streaming has not reached the
popularity it has in China, where livestreaming has a tremendous impact on the
social behaviors of users. To better understand this socio-technological
phenomenon, we conducted a mixed methods study of live streaming practices in
China. We present the results of an online survey of 527 live streaming users,
focusing on their broadcasting or viewing practices and the experiences they
find most engaging. We also interviewed 14 active users to explore their
motivations and experiences. Our data revealed the different categories of
content that was broadcasted and how varying aspects of this content engaged
viewers. We also gained insight into the role reward systems and fan group-chat
play in engaging users, while also finding evidence that both viewers and
streamers desire deeper channels and mechanisms for interaction in addition to
the commenting, gifting, and fan groups that are available today.

通过对中国用户的在线调查和访谈，研究了直播的实践和体验，揭示了其内容类别和激励系统如何吸引用户，以及用户希望更深层次互动的需求。