When they go high, we go low

Lim, May Li; Akcay, Mehmet N.; Bentaleb, Abdelhak; Begen, Ali C.; Zimmermann, Roger

doi:10.1145/3339825.3397043

Cited by 41 publications

(12 citation statements)

References 12 publications

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“…The Low-on-Latency or, in short, LoL ABR algorithm [24] was a significant step forward in multi-bitrate low-latency live streaming. Low-on-Latency Plus (LoLP) [25] is the enhanced version of the LoL ABR algorithm, which tries to implement improvements in three main areas: LoL used hard-coded 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.…”

Section: ) Low-on-latency Plusmentioning

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: ) Low-on-latency Plusmentioning

confidence: 99%

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

2023

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“…In contrast, when using chunked encoding/packaging and delivery for LLL streaming as depicted in Fig. 18, the media player fetches the live-edge segment where the chunks are transmitted in two different transmission phases [31], [32], [106]. First, in the network-limited phase, the available chunks of the requested segment are sent by the server (e.g., first the origin and then the CDN server) at the full network speed to the media player.…”

Section: Media Over Quic (Moq)mentioning

confidence: 99%

“…However, it suffered bandwidth underestimation problems when the idle times were longer. To rectify this issue, first, the Low-on-Latency (LoL) solution [106] and then its extension, LoL + [25], were developed. The LoL and LoL + solutions are learning-based and work efficiently for both short and long inter-chunk idle times, thanks to:…”

Section: Media Over Quic (Moq)mentioning

confidence: 99%

“…The recent ABR schemes designed explicitly for LLL streaming can be categorized as heuristic-based or learning-based. Heuristic-based schemes (e.g., LoL heuristicbased [106], Stallion [73] and Fleet [103]) use mathematical rules or approximations to determine the next best bitrate to request from the server. In an earlier study, Peng et al [137] used the hybrid control theory to formulate the ABR decision problem for low latency.…”

Section: B Rate Adaptationmentioning

confidence: 99%

“…In contrast, learning-based schemes (e.g., LoL learningbased [106], L2A [94], Fugu [184] and ALVS [134]) learn from changes in the system environment to make decisions based on past data. More specifically, leveraging advances in deep reinforcement learning (DRL), Jiang et al [92], Hong et al [78], Zhao et al [188], and Wang et al [178] proposed the following ABR schemes for LLL streaming: HD3, CBLC, L2AC and BitLat, respectively.…”

Section: B Rate Adaptationmentioning

confidence: 99%

See 2 more Smart Citations

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

Bentaleb

Akcay

Lim

et al. 2022

IEEE Trans. Multimedia

Self Cite

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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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Low Latency Low Loss Media Delivery Utilizing In-Network Packet Wash

Clayman

Sayıt

2023

J Netw Syst Manage

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This paper presents new techniques and mechanisms for carrying streams of layered video using Scalable Video Coding (SVC) from servers to clients, utilizing the Packet Wash mechanism which is part of the Big Packet Protocol (BPP). BPP was designed to handle the transfer of packets for high-bandwidth, low-latency applications, aiming to overcome a number of issues current networks have with high precision services. One of the most important advantages of BPP is that it allows the dynamic adaption of packets during transmission. BPP uses Packet Wash to reduce the payload, and the size of a packet by eliminating specific chunks. For video, this means cutting out specific segments of the transferred video, rather than dropping packets, as happens with UDP based transmission, or retrying the transmission of packets, as happens with TCP. The chunk elimination approach is well matched with SVC video, and these techniques and mechanisms are utilized and presented. An evaluation of the performance is provided, plus a comparison of using UDP or TCP, which are the other common approaches for carrying media over IP. Our main contributions are the mapping of SVC video into BPP packets to provide low latency, low loss delivery, which provides better QoE performance than either UDP or TCP, when using those techniques and mechanisms. This approach has proved to be an effective way to enhance the performance of video streaming applications, by obtaining continuous delivery, while maintaining guaranteed quality at the receiver. In this work we have successfully used an H264 SVC encoded video for layered video transmission utilizing BPP, and can demonstrate video delivery with low latency and low loss in limited bandwidth environments.

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When they go high, we go low

Cited by 41 publications

References 12 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

Low Latency Low Loss Media Delivery Utilizing In-Network Packet Wash

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