# Twitch invites you to take on the ICME 2018 Grand Challenge

DevFeed: [Twitch invites you to take on the ICME 2018 Grand Challenge](<https://devfeed.tech/articles/twitch-invites-you-to-take-on-the-icme-2018-grand-challenge-20457.md>)

Original publisher: [Read original article](<https://medium.com/twitch-news/twitch-invites-you-to-take-on-the-icme-2018-grand-challenge-2b3824d3537b?source=rss----3ae745429979--engineering>)

Author: Yueshi Shen

Published: 2017-12-01T17:56:37Z

Content type: article

Language: en

Sources: [Twitch](<https://devfeed.tech/sources/twitch.md>)

Topics: [Streaming](<https://devfeed.tech/topics/streaming.md>), [HTTP](<https://devfeed.tech/topics/http.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [Playback](<https://devfeed.tech/topics/playback.md>), [Algorithms, Complexity](<https://devfeed.tech/topics/algorithms-complexity.md>)

Tags: [caching](<https://devfeed.tech/tags/caching.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [http](<https://devfeed.tech/tags/http.md>), [latency](<https://devfeed.tech/tags/latency.md>), [live-streaming](<https://devfeed.tech/tags/live-streaming.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [network](<https://devfeed.tech/tags/network.md>), [playback](<https://devfeed.tech/tags/playback.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

## AI overview

Twitch presents the ICME 2018 Grand Challenge, which seeks signal-processing and machine-learning algorithms to estimate download bandwidth for HTTP chunked video streaming. The article explains that chunked transfer can reduce live-streaming latency but produces noisy throughput measurements that complicate adaptive bitrate playback.

## Source excerpt

Because of its scalability and low cost, HTTP streaming is now widely used for live broadcasting on the open Internet. A number of platforms including Twitch, Twitter, and Amazon Video are now providing live video services based on HLS or DASH for both user-generated content (UGC) and professionally-generated content (PGC). However, in comparison to more traditional live streaming protocols such as RTMP, HLS and DASH incur much longer end-to-end broadcast latency. The latency is typically 10-30 seconds depending on implementations. On the other hand, low (less than 5 second) broadcast latency is highly demanded as it is critical to interactivity between a broadcaster and their audience. One of the most successful efforts to reduce the latency of HTTP streaming is to utilize the HTTP chunked transfer coding, which enables a video segment to be generated and transmitted concurrently. For more technical details, please read Twitter's recent technical blog "Introducing LHLS Media Streaming." However, compared with segment-based HTTP download, chunked transfer coding makes the bandwidth estimation a lot harder for any ABR playback algorithm. This Grand Challenge is to call for signal-processing/machine-learning algorithms that can effectively estimate download bandwidth based on the noisy samples of chunked-based download throughput. Problem Description: For segment-based HTTP streaming, the download bandwidth can simply be calculated as Bandwidth = segment size / segment download time This is, in general, quite accurate, since the size of a video segment is typically large enough (1 or more seconds of video data). On the other hand, in HTTP chunked transfer coding, a video segment is transmitted in multiple smaller pieces, i.e. the response to one HTTP request is broken into a series of chunks (see Figure 1). Figure 1: Use HTTP chunked transfer to transmit a video segment in multiple chunks from Video Source / HTTP Origin through CDN to Player As we can see from Figure