High-Quality Live Video Streaming via Transcoding Time Prediction and
  Preset Selection
Zahra Nabizadeh Shahre-Babak Nader Karimi Krishna Rapaka Tarek Amara Shadrokh Samavi Shahram Shirani
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.