Dynamic Segment Repackaging at the Edge for HTTP Adaptive Streaming

Aguilar-Armijo, Jesus; Taraghi, Babak; Timmerer, Christian; Hellwagner, Hermann

doi:10.1109/ism.2020.00009

Cited by 5 publications

(3 citation statements)

References 10 publications

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“…It records comprehensive logs from the test streaming session and provides visualized statistics over the results on the Bitmovin Analytics 1 dashboard or similar applications. In another work [10], the authors further developed the functionalities of CAdViSE to suit their project needs, leading to the implementation of a new network node (an edge) in the testbed for repackaging the content more efficiently. CADLAD [11] is another extension of CAdViSE, which presented the implementation of some parts of the Common Media Client Data (CMCD) standard on this testbed.…”

Section: Related Workmentioning

confidence: 99%

LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

2023

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Live media streaming is a challenging task by itself, and when it comes to use cases that define low-latency as a must, the complexity will rise multiple times. In a typical media streaming session, the main goal can be declared as providing the highest possible Quality of Experience (QoE), which has proved to be measurable using quality models and various metrics. In a low-latency media streaming session, the requirements are to provide the lowest possible delay between the moment a frame of video is captured and the moment that the captured frame is rendered on the client screen, also known as end-to-end (E2E) latency and maintain the QoE. This paper proposes a sophisticated cloud-based and open-source testbed that facilitates evaluating a low-latency live streaming session as the primary contribution. Live Low-Latency Cloud-based Adaptive Video Streaming Evaluation (LLL-CAdViSE) framework is enabled to asses the live streaming systems running on two major HTTP Adaptive Streaming (HAS) formats, Dynamic Adaptive Streaming over HTTP (MPEG-DASH) and HTTP Live Streaming (HLS). We use Chunked Transfer Encoding (CTE) to deliver Common Media Application Format (CMAF) chunks to the media players. Our testbed generates the test content (audiovisual streams). Therefore, no test sequence is required, and the encoding parameters (e.g., encoder, bitrate, resolution, latency) are defined separately for each experiment. We have integrated the ITU-T P.1203 quality model inside our testbed. To demonstrate the flexibility and power of LLL-CAdViSE, we have presented a secondary contribution in this paper; we have conducted a set of experiments with different network traces, media players, ABR algorithms, and with various requirements (e.g., E2E latency (typical/reduced/low/ultra-low), diverse bitrate ladders, and catch-up logic) and presented the essential findings and the experimental results.

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Section: Related Workmentioning

confidence: 99%

LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

2023

Self Cite

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“…While packaging is generally less computationally intensive than encoding, the need to cater to and produce multiple delivery formats may add considerable latency to the workflow. To mitigate this, [5] proposed a solution that first packages the content in a single format and when necessary (e.g., when a player does not support the currently available format), repackages the segments dynamically at the edge. Such solutions may be effective if there is a format far more popular than the others, as this increases the utility of the first packaging.…”

Section: A Media Content Preparation Latency (Mcpl)mentioning

confidence: 99%

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Bentaleb

Akcay

Lim

et al. 2022

IEEE Trans. Multimedia

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Our earlier Low-on-Latency (dubbed as LoL) solution offered an accurate bandwidth prediction and rate adaptation algorithm tailored for live streaming applications that targeted an end-to-end latency of up to two seconds. While LoL was a significant step forward in multi-bitrate low-latency live streaming, further experimentation and testing showed that there was room for improvement in three areas. First, LoL used hardcoded parameters computed from an offline training process in the rate adaptation algorithm and this was seen as a significant barrier in LoL's wide deployment. Second, LoL's objective was to maximize a collective QoE function. Yet, certain use cases have specific objectives besides the singular QoE and this had to be accommodated. Third, the adaptive playback speed control failed to produce satisfying results in some scenarios. Our goal in this paper is to address these areas and make LoL sufficiently robust to deploy. We refer to the enhanced solution as LoL + , which has been integrated to the official dash.js player in v3.2.0.

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“…Through edge caching, as illustrated in Figure 2 , the most popular information is placed close to the end-users, from where the users can acquire it without using the entire network infrastructure. Cloud/edge/fog infrastructure is mainly used to process the information, for example, to predict the most popular data or to process the data, for example, video transcoding [ 34 ] or convert the media formats used by different HTTP streaming systems [ 35 ] to minimize the amount of information cached and thus better use caching resources. For the case of wired transmission (i.e., HTTP streaming, WebRTC, etc.…”

Section: Multimedia Transmission Pipelinementioning

confidence: 99%

Machine Learning for Multimedia Communications

Thomos

Maugey

Toni

2022

Sensors

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Machine learning is revolutionizing the way multimedia information is processed and transmitted to users. After intensive and powerful training, some impressive efficiency/accuracy improvements have been made all over the transmission pipeline. For example, the high model capacity of the learning-based architectures enables us to accurately model the image and video behavior such that tremendous compression gains can be achieved. Similarly, error concealment, streaming strategy or even user perception modeling have widely benefited from the recent learning-oriented developments. However, learning-based algorithms often imply drastic changes to the way data are represented or consumed, meaning that the overall pipeline can be affected even though a subpart of it is optimized. In this paper, we review the recent major advances that have been proposed all across the transmission chain, and we discuss their potential impact and the research challenges that they raise.

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Dynamic Segment Repackaging at the Edge for HTTP Adaptive Streaming

Cited by 5 publications

References 10 publications

LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

LLL-CAdViSE: Live Low-Latency Cloud-Based Adaptive Video Streaming Evaluation Framework

Catching the Moment With LoL$^+$ in Twitch-Like Low-Latency Live Streaming Platforms

Machine Learning for Multimedia Communications

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