Low Latency streaming
One of the major challenges in OTT streaming is reducing the live streaming latency. This can be crucial for live events like sport games or for an optimal streamer-user interaction in eSports games.
A live event is distributed over DASH as well over regular TV distribution. The event should play-out approximately at the same time on both devices in order to avoid different perceptions of the same service when received over different distribution means. The objective should be to get to a range of delay for the DASH based service that is equivalent to cable and IPTV services [1].
Sports bars are commonly in close proximity to each other and may all show the same live sporting event. Some bars may be using a provider which distributes the content using DVB-T or DVB-S services whilst others may be using DASH ABR. Viewers in a bar with a high latency will have their viewing spoiled as they will hear cheers for the goal before it occurs on their local screen.
This creates a commercial incentive for the bar operator to switch to the provider with the lowest latency. The objective should be to get the latency range to not be perceptibly different to that of a DVB broadcast solution for those users who have a sufficient quality (high and consistent speed) connection [1].
Professional streamers interacting with a live audience on social media, often via a directly coupled chat function in the viewing app/environment. They can generate direct revenue in several ways including:
- In stream advertising
- Micropayments (for example Twitch “bits”)
A high degree of interactivity between the performer and the audience is required to enable engagement. Lower latencies increases the engagement and consequently the incentive for the audience members to reward the performer with likes, shares, subscribes, micropayments, etc.
Typical use cases include gamers, musicians and other performers where in some part the direction of the performance can be guided by the audience response [1].
A provider wants to offer a live stream that will be used for wagering within an event. The content must be delivered with low latency and more importantly within a well-defined sync across endpoints so customers trust the game is fair. There are in some cases legal considerations, for example the content cannot be shown if it is more than X seconds behind live.
Visual and aural quality are secondary in priority in these scenarios to sync and latency. The lower the latency the more opportunities for “in play betting” within the game/event. This in turn increases revenue potential from a game/event [1].
The Common Media Application Format introduces the concept of "chunks". A CMAF chunk has multiple "moof" and "mdat" boxes, allowing the client to access the media data before the segment is completely finished. The benefits of the chunked mode become more obvious when looking at a concrete example:
So let’s assume we have 8 second segments and we are currently 3 seconds into segment number four. For classic media segments, this leaves us with two options:
- Option 1: since segment four is not completed, we start with segment three. That way, we end up 11 seconds behind the live edge – 8 seconds coming from segment three, and 3 seconds coming from segment four.
- Option 2: we wait for segment four to finish and immediately start downloading and playing it. We end up with 8 seconds of latency and a waiting time of 5 seconds.
With CMAF chunks, on the other hand, we are able to play segment four before it is completely available. In the example above, we have CMAF chunks with a 1 second duration, which leads to eight chunks per segment. Let’s assume that only the first chunk contains an IDR frame and therefore we always need to start the playback from the beginning of a segment. Being three seconds into segment four leaves us with 3 seconds of latency. That’s much better than what we achieved with classic segments. We can also fast decode the first chunks and play even closer to the live edge [2].
dash.js supports CMAF low latency streaming since version 2.6.8. For that reason, a dedicated sample page is available:
The following Sections below will give a detailed explanation on L2ALL and LoL+. Some parameters are valid for all low latency algorithms:
| Parameter | Description |
|---|---|
| liveDelay | Lowering this value will lower latency but may decrease the player's ability to build a stable buffer. |
| maxDrift | Maximum latency deviation allowed before dash.js to do a seeking to live position |
| playbackRate | Catch-up rate, as a percentage, for low latency live streams. |
The corresponding API call looks the following:
player.updateSettings({
streaming: {
delay: {
liveDelay: 4
},
liveCatchup: {
maxDrift: 0,
playbackRate: {
min: 0.5,
max: 0.5
}
}
}
});Please check the API documentation for additional information.
It is also possible to configure specific low latency settings via MPD. The required information is encapsulated in a <ServiceDescription> element:
<ServiceDescription id="0">
<Latency max="6000" min="2000" referenceId="0" target="4000" />
<PlaybackRate max="1.04" min="0.96" />
</ServiceDescription>For more details please refer to the DASH-IF IOP guidelines [4].
In order to use dash.js in low latency mode the following requirements have to be fullfilled:
- The Fetch API must be supported on the client browser.
- The server must support HTTP 1.1 Chunked transfer encoding
The content and the manifest must be conditioned to support CMAF low latency chunks
The manifest must contain two additional attributes
- @availabilityTimeComplete: specifies if all segments of all associated representations are complete at the adjusted availability start time. If the value is set to false, then it may be inferred by the client that the segment is available at its announced location prior to completion.
- @availabilityTimeOffset (ATO): provides the time in how much earlier segments are available compared to their computed availability start time (AST).
The segments must contain multiple CMAF chunks. This will result in multiple "moof" and "mdat" boxes per segment. Example
[styp] size=8+16
[prft] size=8+24
[moof] size=8+96
[mfhd] size=12+4
sequence number = 827234
[traf] size=8+72
[tfhd] size=12+16, flags=20038
track ID = 1
default sample duration = 1001
default sample size = 15704
default sample flags = 1010000
[tfdt] size=12+8, version=1
base media decode time = 828060233
[trun] size=12+12, flags=5
sample count = 1
data offset = 112
first sample flags = 2000000
[mdat] size=8+15704
[prft] size=8+24
[moof] size=8+92
[mfhd] size=12+4
sequence number = 827235
[traf] size=8+68
[tfhd] size=12+16, flags=20038
track ID = 1
default sample duration = 1001
default sample size = 897
default sample flags = 1010000
[tfdt] size=12+8, version=1
base media decode time = 828061234
[trun] size=12+8, flags=1
sample count = 1
data offset = 108
[mdat] size=8+897
[prft] size=8+24
[moof] size=8+92
[mfhd] size=12+4
sequence number = 827236
[traf] size=8+68
[tfhd] size=12+16, flags=20038
track ID = 1
default sample duration = 1001
default sample size = 7426
default sample flags = 1010000
[tfdt] size=12+8, version=1
base media decode time = 828062235
[trun] size=12+8, flags=1
sample count = 1
data offset = 108
[mdat] size=8+7426
Compared to ABR algorithms for "classic" live streaming an ABR algorithm for low latency streaming has to overcome additional challenges.
Common throughput based ABR algorithms calculate the available bandwidth on the client side using the download time for a segment:
Calculated Throughput = (Segment@Bitrate*Segment@duration) / DownloadTime
Example:
Calculated Throughput = (6Mbit/s * 6s) / 3s = 12 Mbit/s
The concept described above is a problem for clients operating in low latency mode. Since segments are transferred via HTTP 1.1 Chunked transfer encoding the download time of a segment is often times similar to its duration. The download of a segment is started prior to its completion. Therefore, the data is still generated on the server side and arrives in small chunks at the client side .
For instance, the download time for a segment with six second duration will be approximately six seconds. There will be idle times in which no data is transferred from the server to the client. However, the connection remains open while the client waits for new data. The total download time includes these idle times. Consequently, the total download time is not a good indicator for the available bandwidth on the client side.
dash.js offers two different modes for low latency throughput estimation
For every segment that is downloaded the default algorithm saves the timestamp and the length of bytes received throughout the download process. The data packets do not arrive at moof boundaries. For instance a single "data burst" might contain multiple moof/mdat pairs. For every data point an entry in the corresponding array is created:
downloadedData.push({
ts: Date.now(), // timestamp when the data arrived
bytes: value.length // length of the data
});After the download of a segment is completed, the array above is cleared and the throughput is calculated in the following way:
function _calculateDownloadedTimeByBytesReceived(downloadedData, bytesReceived) {
downloadedData = downloadedData.filter(data => data.bytes > ((bytesReceived / 4) / downloadedData.length));
if (downloadedData.length > 1) {
let time = 0;
const avgTimeDistance = (downloadedData[downloadedData.length - 1].ts - downloadedData[0].ts) / downloadedData.length;
downloadedData.forEach((data, index) => {
// To be counted the data has to be over a threshold
const next = downloadedData[index + 1];
if (next) {
const distance = next.ts - data.ts;
time += distance < avgTimeDistance ? distance : 0;
}
});
return time;
}
}- In the first step the downloadedData array is filtered and all entries that do not have a certain size are removed.
- In the next step the average time distance between two consecutive data points is calculated
- If time distance between two consecutive data points is smaller than the average time distance the time distance is added to the total download time
- The total download time is used to calculate the throughput as described before. Using this approach the download time is no longer equal to the duration of the segment.
In contrast to the default throughput algorithm, the moof based throughput estimation is based on saving the download time for each CMAF chunk. For that reason, the start and the endtime of each chunk, starting with a moof box and ending with an mdat box are saved:
// Store the start time of each chunk download
const flag1 = boxParser.parsePayload(['moof'], remaining, offset);
if (flag1.found) {
// Store the beginning time of each chunk download
startTimeData.push({
ts: performance.now(),
bytes: value.length
});
}
const boxesInfo = boxParser.findLastTopIsoBoxCompleted(['moov', 'mdat'], remaining, offset);
if (boxesInfo.found) {
const end = boxesInfo.lastCompletedOffset + boxesInfo.size;
// Store the end time of each chunk download
endTimeData.push({
ts: performance.now(),
bytes: remaining.length
});
}The download time of the segment is calculated the following way:
function _calculateDownloadedTimeByMoofParsing(startTimeData, endTimeData) {
let datum, datumE;
// Filter the first and last chunks in a segment in both arrays [StartTimeData and EndTimeData]
datum = startTimeData.filter((data, i) => i > 0 && i < startTimeData.length - 1);
datumE = endTimeData.filter((dataE, i) => i > 0 && i < endTimeData.length - 1);
// Compute the download time of a segment based on the filtered data [last chunk end time - first chunk beginning time]
let segDownloadTime = 0;
if (datum.length > 1) {
for (let i = 0; i < datum.length; i++) {
if (datum[i] && datumE[i]) {
let chunkDownladTime = datumE[i].ts - datum[i].ts;
segDownloadTime += chunkDownladTime;
}
}
return segDownloadTime;
}
return null;
}The desired throughput calculation mode can be selected by changing the respective settings parameter:
| Value | Mode |
|---|---|
| ABR_FETCH_THROUGHPUT_CALCULATION_DOWNLOADED_DATA | Default throughput estimation |
| ABR_FETCH_THROUGHPUT_CALCULATION_MOOF_PARSING | Moof based throughput estimation |
player.updateSettings({
streaming: {
abr: {
fetchThroughputCalculationMode: Constants.ABR_FETCH_THROUGHPUT_CALCULATION_DOWNLOADED_DATA
}
}
})When playing in low latency mode the client needs to maintain a consistent live edge allowing only small deviations compared to the target latency.
In order to maintain a consistent live edge dash.js either adjusts the playback rate of the video (catchup mechanism), or performs a seek back to the live edge. The catchup behavior of dash.js based on the deviation compared to the target latency is depicted below:
In order to determine whether the catchup mechanism should be enabled the following logic is applied:
function _defaultNeedToCatchUp() {
try {
const latencyDrift = Math.abs(_getLatencyDrift());
return latencyDrift > 0;
} catch (e) {
return false;
}
}In case the catchup mechanism is applied the new playback rate is calculated the following way:
function _calculateNewPlaybackRateDefault(liveCatchUpPlaybackRates, currentLiveLatency, liveDelay, bufferLevel) {
// if we recently ran into an empty buffer we wait for the buffer to recover before applying a new rate
if (playbackStalled) {
return 1.0;
}
const deltaLatency = currentLiveLatency - liveDelay;
const cpr = (deltaLatency < 0) ? Math.abs(liveCatchUpPlaybackRates.min) : liveCatchUpPlaybackRates.max;
const d = deltaLatency * 5;
// Playback rate must be between (1 - cpr) - (1 + cpr)
// ex: if cpr is 0.5, it can have values between 0.5 - 1.5
const s = (cpr * 2) / (1 + Math.pow(Math.E, -d));
let newRate = (1 - cpr) + s;
// take into account situations in which there are buffer stalls,
// in which increasing playbackRate to reach target latency will
// just cause more and more stall situations
if (playbackController.getPlaybackStalled()) {
if (bufferLevel <= liveDelay / 2 && deltaLatency > 0) {
newRate = 1.0;
}
}
return newRate;
}Note that the new playback rate must differ from the current playback rate by a hardcoded threshold:
minPlaybackRateChange = isSafari ? 0.25 : 0.02;
The LoL+ based catchup mechanism follows the same principles as the default catchup mechanism. In the first step dash.js checks if the catchup mechanism should be applied:
function _lolpNeedToCatchUpCustom(currentBuffer, playbackBufferMin) {
try {
const latencyDrift = Math.abs(_getLatencyDrift());
return latencyDrift > 0 || currentBuffer < playbackBufferMin;
} catch (e) {
return false;
}
}Compared to the default catchup mechanism, the LoL+ based catchup check uses playbackBufferMin. If either the latency drift exceeds zero or the current buffer length is smaller than the minimum buffer playbackBufferMin the catchup mode is activated.
Note that a change of playback rate can also mean that the playback rate is decreased. This can be useful to avoid buffer underruns.
The new playback rate is calculated in the following way:
function _calculateNewPlaybackRateLolP(liveCatchUpPlaybackRate, currentLiveLatency, liveDelay, minDrift, playbackBufferMin, bufferLevel, currentPlaybackRate) {
let newRate;
// Hybrid: Buffer-based
if (bufferLevel < playbackBufferMin) {
// Buffer in danger, slow down
const cpr = Math.abs(liveCatchUpPlaybackRates.min); // Absolute value as negative delta value will be used.
const deltaBuffer = bufferLevel - playbackBufferMin; // -ve value
const d = deltaBuffer * 5;
// Playback rate must be between (1 - cpr) - (1 + cpr)
// ex: if cpr is 0.5, it can have values between 0.5 - 1.5
const s = (cpr * 2) / (1 + Math.pow(Math.E, -d));
newRate = (1 - cpr) + s;
logger.debug('[LoL+ playback control_buffer-based] bufferLevel: ' + bufferLevel + ', newRate: ' + newRate);
} else {
// Hybrid: Latency-based
// Buffer is safe, vary playback rate based on latency
const cpr = liveCatchUpPlaybackRates.max;
// Check if latency is within range of target latency
const minDifference = 0.02;
if (Math.abs(currentLiveLatency - liveDelay) <= (minDifference * liveDelay)) {
newRate = 1;
} else {
const deltaLatency = currentLiveLatency - liveDelay;
const d = deltaLatency * 5;
// Playback rate must be between (1 - cpr) - (1 + cpr)
// ex: if cpr is 0.5, it can have values between 0.5 - 1.5
const s = (cpr * 2) / (1 + Math.pow(Math.E, -d));
newRate = (1 - cpr) + s;
}
logger.debug('[LoL+ playback control_latency-based] latency: ' + currentLiveLatency + ', newRate: ' + newRate);
}
return newRate
}If the buffer level is smaller than the buffer level defined in playbackBufferMin the playback rate is decreased. If the buffer is "safe", the playback rate is adjusted depending on the latency.
Both catchup mechanisms share a common method to determine the calculation of the new playback rate:
const s = (cpr * 2) / (1 + Math.pow(Math.E, -d));
newRate = (1 - cpr) + s;-
cpris defined as the catchupplaybackRate -
dis defined as a multiple of the delta latency or the delta buffer -
Math.Erepresents the base of natural logarithms, e, approximately 2.718.
If the current live latency is larger than the target latency then d is positive, otherwise d is negative. Consequently, if the playback rate needs to be incremented to reach the target latency the equation Math.pow(Math.E, -d) will result in values smaller than 1. For negative d values, situations in which the playback rate should be decreased, the equation Math.pow(Math.E, -d) will result in values greater than 1.
As an example consider a situation in which the target latency is set to 2 seconds and the current latency equals 5 seconds:
cpr = 0.5
delta latency = current latency - target latency = 5 - 2 = 3
d = delta latency * 5 = 3 * 5 = 15
s = (cpr * 2) / (1 + Math.pow(Math.E, -d)) = (0.5 * 2) / (1 + 3.0590232050182605e-7) = 0.999999694097773
new rate = (1 - cpr) + s = (1 - 0.5) + 0.999999694097773 = 1.499999694097773
The new rate will always stay within the target boundaries 1 +/- 0.5
The desired catchup mechanism can be selected by changing the respective settings parameter:
| Value | Mode |
|---|---|
| LIVE_CATCHUP_MODE_DEFAULT | Default catchup mechanism |
| LIVE_CATCHUP_MODE_LOLP | LoL+ based catchup mechanism |
player.updateSettings({
streaming: {
liveCatchup: {
mode: Constants.LIVE_CATCHUP_MODE_DEFAULT
}
}
})In addition, both catchup algorithms share a common logic to seek back to the live edge. If the latency delta exceeds the threshold defined in maxDrift the seek is performed:
// we reached the maxDrift. Do a seek
const maxDrift = mediaPlayerModel.getCatchupMaxDrift();
if (!isNaN(maxDrift) && maxDrift > 0 &&
deltaLatency > maxDrift) {
logger.info('[CatchupController]: Low Latency catchup mechanism. Latency too high, doing a seek to live point');
isCatchupSeekInProgress = true;
_seekToLive();
}With dash.js version 3.2.0 two low latency specific algorithms were implemented. A description of both algorithms LoL+ and L2A can found in the following.
LoL+ is designed as a series of sophisticated yet robust player improvements for low latency live (LLL) streaming. LoL+ consists of five essential modules:
- The bitrate selection module implements a learning-based ABR algorithm to choose a suitable bitrate at each segment download. The ABR algorithm is based on an SOM model that considers multiple QoE metrics as well as bandwidth variability.
- The playback speed control module implements a hybrid algorithm that considers both the current latency and buffer level to control the playback speed.
- The throughput measurement module accurately calculates the throughout by removing the idle times between the chunks of a segment through a three-step algorithm.
- The QoE evaluation module computes the QoE considering five key metrics: selected bitrate, number of bitrate switches, rebuffering duration, latency and playback speed.
- Lastly, the weight selection module implements a two-step dynamic weight assignment for the SOM model features. We also added manual (equal value of 0.4 each) and random (based on Xavier formula) weight assignment for the SOM model features for comparison.
The modules can be found in the following files (as of dash.js v3.2):
- Bitrate selection module (i.e., ABR algorithm):
dash.js/src/streaming/rules/abr/lolp/LoLpRule.jsandLearningAbrController.js - Playback speed control module:
dash.js/src/streaming/controllers/CatchupController.js - Throughput measurement module:
dash.js/src/streaming/net/FetchLoader.js - QoE evaluation module:
dash.js/src/streaming/rules/abr/lolp/LoLpQoeInfo.jsanddash.js/src/streaming/rules/abr/lolp/LoLpQoEEvaluator.js - Weight selection module:
dash.js/src/streaming/rules/abr/lolp/LoLpWeightSelector.js
How to enable each of the LoL+ modules:
// Code snippet from the samples Section `lolp_index.html`
player.updateSettings({
streaming: {
buffer: {
stallThreshold: 0.05 // used in BufferController for more accurate stall tracking in low-latency live streaming
}
abr: {
useDefaultABRRules: true,
ABRStrategy: 'abrLoLP', // to enable LoLp bitrate selection module
fetchThroughputCalculationMode: 'abrFetchThroughputCalculationMoofParsing' // to enable LoLp throughput measurement module
},
liveCatchup: { // to enable LoLp playback speed control module
playbackBufferMin: playbackBufferMin,
mode: 'liveCatchupModeLoLP'
}
}
});Note: The weight selection module is used in the bitrate selection module and does not need to be enabled separately.
An example for low latency streaming with LoL+ can be found here
| Parameter | Description | Recommended value |
|---|---|---|
| liveDelay | Lowering this value will lower latency but may decrease the player's ability to build a stable buffer. Note that this value affects several dash.js components (in addition to the calculation of playback rate), such as deriving which segment to request based on deviation from the live edge. | 1.5s-2s |
| playbackRate | Maximum catch-up rate, as a percentage, for low latency live streams. | 0.3 (i.e., 0.7x-1.3x). |
| playbackBufferMin | Minimum buffer allowed before activating buffer-based playback speed control (in place of latency-based). | 0.5s |
The corresponding API call looks the following:
player.updateSettings({
streaming: {
delay: {
liveDelay: 2
},
liveCatchup: {
playbackRate: {
min: 0.3
max: 0.3
},
playbackBufferMin: 0.5
}
}
});The parameters below are available but not advised to be changed. For advanced users, please refer to the paper [3] for further details on these parameters. The following parameters are not exposed in the Settings object and have to be changed in the respective classes.
| Module | Parameter(s) | Remarks |
|---|---|---|
| Bitrate selection module | targetLatency = 0 | SOM target latency |
| Bitrate selection module | targetRebufferLevel = 0 | SOM target rebuffering duration. |
| Bitrate selection module | targetSwitch = 0 | SOM target number of switches. |
| Bitrate selection module | throughputDelta = 10000 | SOM variation in throughput. |
| Weight selection module | DWS_TARGET_LATENCY = 1.5 | Latency constraint used in weight selection module (set in LolpRule.js). |
| Weight selection module | DWS_BUFFER_MIN = 0.3 | Minimum buffer value constraint used in weight selection module (set in LolpRule.js). |
| Weight selection module | weightObj.throughput, weightObj.latency, weightObj.buffer, weightObj.switch | The weight (value ranges between 0 and 1) of the current throughput, latency, rebuffering duration, and number of switches features of the SOM model (Bitrate selection module) will be assigned dynamically by this module based on the defined optimization function. |
The ABR algorithm used in LoL+ bitrate selection module is explained below in pseudocode and code snippets:
At each segment download, the bitrate selection module is triggered and works as follows:
- Current player state: throughput, latency, rebuffer duration, bitrate variation (normalized)
- Weight vector from weight selection module: w
// Code snippet from `LearningAbrController.js`
_updateNeurons(currentNeuron, somElements, [throughputNormalized, latency, rebuffer, bitrateSwitch]);(3) Iterate all neurons to find the winner neuron that is closest (i.e., shortest distance) to the target state, while not violating special condition (pseudocode lines 12-27):
// Code snippet from `LearningAbrController.js`
// special condition downshift immediately
if (somNeuron.bitrate > throughput - throughputDelta || isBufferLow) {
if (somNeuron.bitrate !== minBitrate) {
// encourage to pick smaller bitrates throughputWeight=100
distanceWeights[0] = 100;
}
}
// calculate the distance with the target
let distance = _getDistance(somData, [throughputNormalized, targetLatency, targetRebufferLevel, targetSwitch], distanceWeights);
if (minDistance === null || distance < minDistance) {
minDistance = distance;
minIndex = somNeuron.qualityIndex;
winnerNeuron = somNeuron;
winnerWeights = distanceWeights;
}// Code snippet from `LearningAbrController.js`
_updateNeurons(winnerNeuron, somElements, [throughputNormalized, targetLatency, targetRebufferLevel, bitrateSwitch]);Note: The QoE evaluation module is provided but QoE score is not used as a SOM factor in the current implementation of the ABR algorithm. Advanced users may consider using it.
In the context of adaptive streaming, an ABR algorithm aims at seamlessly adjusting (or adapting) the rate of the media stream, to compensate for changing network conditions. Additionally, a buffer is typically deployed to protect the client from abrupt changes in the communication channel (throughput, jitter etc.), or temporal misestimations of the ABR algorithm. Since long buffer queues compound delay of the media rendering process, low-latency streaming requires very short buffers, that in turn offer less protection against channel state estimation errors. Such errors are propagated to the ABR decisions, that in turn can have a detrimental effect on streaming experience.
Therefore the goal of Learn2Adapt-LowLatency (L2A-LL), a low-latency ABR, is to strike a favorable balance between keeping the buffer as short as possible, while provisioning against its complete depletion. This is achieved by selecting the highest sustainable bitrate for each video fragment, that does not completely consume the buffer budget available at the time of request. L2A-LL is, in essence, an optimization solution with the objective of minimizing latency, while at the same time maximizing achievable video bitrate and ensuring uninterrupted and stable streaming.
L2A-LL formulates the ABR optimization problem under an online (machine) learning framework, based on convex optimization. First, the streaming client is modelled by a learning agent, whose objective is to minimize the average buffer displacement of a streaming session. Second, certain requirements regarding the decision set (available bitrates) and constraint functions are fulfilled by a) allowing the learning agent to make decisions on the video bitrate of each fragment, according to a probability distribution and by b) deriving an appropriate constraint function associated with the upper bound of the buffer queue, that adheres to time averaging constraints.
How to enable L2A:
player.updateSettings({
streaming: {
abr: {
useDefaultABRRules: true,
ABRStrategy: 'abrL2A' // to enable L2A bitrate selection module
}
}
});An example for low latency streaming with L2A can be found here
The following changes are for experienced users and need to be made in streaming/rules/abr/L2ARule.js. Please check the paper [5] for further details.
| Line | Parameter | Description | Conclusion |
|---|---|---|---|
| 306 | const horizon=4 | Optimization horizon: This parameter is used to specify the amount of steps required to achieve convergence. In live streaming settings this parameter must be kept low for stable performance. The selected value has been verified experimentally and alteration is not suggested. This parameter is used in lines 307 and 308, at the calculation of the 'vl' and 'alpha' parameters respectively. The calculation of 'vl' and 'alpha' are according to the theoretical performance guarantees as specified in the MMSys Publication [5]. Higher 'vl' values make the algorithm more aggressive in the bitrate selection and 'alpha' is the step size of the gradient descent approach of the learning process. | higher 'horizon' leads to more aggressive bitrate selection (higher 'vl') and less exploration (large 'alpha'). Not advisable for live streaming scenarios with short buffers. |
| 322 | const react = 2 | Reactiveness to volatility (abrupt throughput drops). This parameter is used to recalibrate the 'l2AParameter.Q'. Higher values make the algorithm more conservative. The chosen value has been experimentally selected and alteration is not suggested. | Higher 'react' results in a more conservative algorithm (higher l2AParameter.Q). Caution: 'react' values higher than the selected (react=2) may make the algorithm select lowest bitrate for extended periods, until recovery of the l2AParameter.Q (updated at every fragment download) |
| Description | Segment length | Chunk length | Url |
|---|---|---|---|
| Akamai Low Latency Stream (Single Rate) | 2.002s | 0.033s | https://akamaibroadcasteruseast.akamaized.net/cmaf/live/657078/akasource/out.mpd |
| Akamai Low Latency Stream (Multi Rate) | 2.002s | 0.033s | https://cmafref.akamaized.net/cmaf/live-ull/2006350/akambr/out.mpd |
| Low Latency (Single-Rate) (livesim-chunked) | 8s | 1s | https://livesim.dashif.org/livesim/chunkdur_1/ato_7/testpic4_8s/Manifest300.mpd |
| Low Latency (Multi-Rate) (livesim-chunked) | 8s | 1s | https://livesim.dashif.org/livesim/chunkdur_1/ato_7/testpic4_8s/Manifest.mpd |
- Daniel Silhavy - dash.js – Low Latency Streaming with CMAF
- Will Law - Using LL-HLS with byte-range addressing to achieve interoperability in low latency streaming
- Theo Karagkioules,R. Mekuria,Dirk Griffioen, Arjen Wagenaar - Online learning for low-latency adaptive streaming
- May Lim, Mehmet N Akcay, Abdelhak Bentaleb, Ali C. Begen, R. Zimmermann - When they go high, we go low: low-latency live streaming in dash.js with LoL
- Will Law - Chunky Monkey
- Theo Karagkioules,R. Mekuria,Dirk Griffioen, Arjen Wagenaar - Online learning for low-latency adaptive streaming
- May Lim, Mehmet N Akcay, Abdelhak Bentaleb, Ali C. Begen, R. Zimmermann - When they go high, we go low: low-latency live streaming in dash.js with LoL
- [1] DASH-IF - Report on Low-Latency Live Service with DASH
- [2] Daniel Silhavy - dash.js – Low Latency Streaming with CMAF
- [3] Abdelhak Bentaleb, Mehmet N. Akcay, May Lim, Ali C. Begen, R. Zimmermann - Catching the Moment with LoL + in Twitch-Like Low-Latency Live Streaming Platforms (to appear in IEEE Trans. Multimedia - pdf)
- [4] DASH-IF DASH Live Services
- [5] Theo Karagkioules,R. Mekuria,Dirk Griffioen, Arjen Wagenaar - Online learning for low-latency adaptive streaming