A Subjective Comparison of Broadcast and Unicast Transmission Impairments

Allan, Brahim; Nilsson, Mike; Kegel, Ian

doi:10.5594/jmi.2019.2910704

Cited by 4 publications

(5 citation statements)

References 6 publications

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“…Therefore, viewing engagement with the app VoD content may be increased by delivering items such that the number of interruptions is minimised whilst remaining within an envelope of buffering duration. We note that the same result has been observed in [2] through subjective studies using assets of fixed duration.…”

Section: F Buffering Duration Vs Number Of Interruptionssupporting

confidence: 87%

“…Viewers who choose to stream content on the app may do so in the absence of a TV or when mobile and are more invested in the platform, potentially due to a lack of options in viewing live content. Therefore frustration due to buffering may not result in disengagement or may indeed be seen as normal for an Internet streaming experience [2].…”

Section: B Loss-related Errors and Long-term Live Tv Engagement By Pmentioning

confidence: 99%

“…Irrespective of end-to-end heterogeneity, video quality delivered across multiple platforms to a converged customer should to be equivalent from the customer's perspective, suggesting the need for cross-platform benchmarking of end-to-end video quality. However, customer expectations and context play a key role in benchmarking -the same viewer has a different tolerance to (re)buffering experienced on an application (app) on a portable device whilst travelling on a train than on a STB connected to a family room television during prime time [2]. A viewer may also pay different levels of attention in both scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

Karthikeyan

Allan

Nauck

et al. 2020

IEEE Trans. Netw. Serv. Manage.

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We present the first empirical study of the impact of loss-related errors on TV viewing engagement across disparate platforms, delivery technologies and performance measures. Our dataset comprises anonymised video viewing sessions and data about quality of delivery from a content service provider with a nationwide customer base. We study buffering events on streaming apps, mild and severe packet loss errors on multicastdelivered IPTV to a Set-Top-Box (STB) and signal strength errors on Digital Terrestrial TV. Since these metrics cannot be directly compared to each other, we use engagement as our proxy measure. We first characterise the relationship between each impairment and viewing engagement, investigating confounding factors such as type of content, asset length and connection type. We conclude that the loss of engagement due to poor quality delivery is incurred immediately for on-demand content and in the long-term for live content. We rank impairments across platforms by their impact on engagement.

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Section: F Buffering Duration Vs Number Of Interruptionssupporting

confidence: 87%

Section: B Loss-related Errors and Long-term Live Tv Engagement By Pmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

Karthikeyan

Allan

Nauck

et al. 2020

IEEE Trans. Netw. Serv. Manage.

Self Cite

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show abstract

“…Quality of Experience factors in video streaming are an active area of research. Allan et al [13] conducted a subjective test with 630 participants to nd out how di erent QoE factors a ect users. They concluded that rebu ering events are the most annoying to users, and that two short rebu ering events are more annoying than one long rebu ering event of the same duration.…”

Section: Qoe Factorsmentioning

confidence: 99%

Evaluation of CMAF in live streaming scenarios

Lyko

Broadbent

Race

et al. 2020

Proceedings of the 30th ACM Workshop on Network and Operating Systems Support for Digital Audio and Video

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HTTP Adaptive Streaming (HAS) technologies such as MPEG DASH are now used extensively to deliver television services to large numbers of viewers. In HAS, the client requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-o video quality with the avoidance of stalling. This introduces signi cant end to end latency compared to traditional broadcast, due to the the client requiring a large enough bu er for the ABR algorithm to react to changes in network conditions in a timely manner. The recently standardised Common Media Application Format (CMAF) has helped address the issue of latency by de ning segments as composed of independently transferable chunks. In this paper, we describe a simulation model we have developed to evaluate the performance of four popular ABR algorithms using DASH and CMAF in various low latency live streaming scenarios. Realistic network conditions are used for the evaluation, which are based on throughput data taken from the CDN logs of a commercial live TV service. We quantify the performance of the ABR algorithms using a selection of QoE metrics, and show that CMAF can signi cantly improve ABR performance in low delay scenarios. CCS CONCEPTS• Information systems → Multimedia streaming.

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“…Quality of Experience factors in video streaming are an active area of research. Allan et al [10] conducted a subjective test with 630 participants to investigate how different QoE factors affect users. They concluded that rebuffering events were the most annoying to users.…”

Section: B Qoe Factorsmentioning

confidence: 99%

Llama - Low Latency Adaptive Media Algorithm

Lyko

Broadbent

Race

et al. 2020

2020 IEEE International Symposium on Multimedia (ISM)

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In the recent years, HTTP Adaptive Bit Rate (ABR) streaming including Dynamic Adaptive Streaming over HTTP (DASH) has become the most popular technology for video streaming over the Internet. The client device requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces high latency compared to traditional broadcast methods, mostly in the client buffer which needs to hold enough data to absorb any changes in network conditions. Clients employ an ABR algorithm which monitors network conditions and adjusts the quality at which segments are requested to maximise the user's Quality of Experience. The size of the client buffer depends on the ABR algorithm's capability to respond to changes in network conditions in a timely manner, hence, low latency live streaming requires an ABR algorithm that can perform well with a small client buffer. In this paper, we present Llamaa new ABR algorithm specifically designed to operate in such scenarios. Our new ABR algorithm employs the novel idea of using two independent throughput measurements made over different timescales. We have evaluated Llama by comparing it against four popular ABR algorithms in terms of multiple QoE metrics, across multiple client settings, and in various network scenarios based on CDN logs of a commercial live TV service. Llama outperforms other ABR algorithms, improving the P.1203 Mean Opinion Score (MOS) as well as reducing rebuffering by 33% when using DASH, and 68% with CMAF in the lowest latency scenario.

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A Subjective Comparison of Broadcast and Unicast Transmission Impairments

Cited by 4 publications

References 6 publications

Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

Benchmarking Video Service Quality: Quantifying the Viewer Impact of Loss-Related Impairments

Evaluation of CMAF in live streaming scenarios

Llama - Low Latency Adaptive Media Algorithm

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