English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Video streaming often requires transcoding content into different resolutionsand bitrates to match the recipient's internet speed and screen capabilities.Video encoders like x264 offer various presets, each with different tradeoffsbetween transcoding time and rate-distortion performance. Choosing the bestpreset for video transcoding is difficult, especially for live streaming, astrying all the presets and choosing the best one is not feasible. One solutionis to predict each preset's transcoding time and select the preset that ensuresthe highest quality while adhering to live streaming time constraints.Prediction of video transcoding time is also critical in minimizing streamingdelays, deploying resource management algorithms, and load balancing. Wepropose a learning-based framework for predicting the transcoding time ofvideos across various presets. Our predictor's features for video transcodingtime prediction are derived directly from the ingested stream, primarily fromthe header or metadata. As a result, only minimal additional delay is incurredfor feature extraction, rendering our approach ideal for live-streamingapplications. We evaluated our learning-based transcoding time prediction usinga dataset of videos. The results demonstrate that our framework can accuratelypredict the transcoding time for different presets, with a mean absolutepercentage error (MAPE) of nearly 5.0%. Leveraging these predictions, we thenselect the most suitable transcoding preset for live video streaming. Utilizingour transcoding time prediction-based preset selection improved PeakSignal-to-Noise Ratio (PSNR) of up to 5 dB.

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