Modelling the impact of caching and popularity on concurrent adaptive multimedia streams in Information-Centric Networks

Kreuzberger, Christian; Rainer, Benjamin; Hellwagner, Hermann

doi:10.1109/icmew.2015.7169763

Cited by 11 publications

(5 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, Jia et al [25] designed a control layer for optimal Interest forwarding and adaptive video caching based on the virtual queue of each bit rate. Kreuzberge et al [26] developed a cache-aware traffic-shaping policy in response to the unfair bandwidth sharing generated by rate-adaptive video streams. Liu et.al.…”

Section: Related Workmentioning

confidence: 99%

Quality of Experience in ICN: Keep Your Low- Bitrate Close and High-Bitrate Closer

Oteafy

Fayed

et al. 2021

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

Recent studies into streaming media delivery suggest that performance gains from ubiquitous caching in Information-Centric Networks (ICN) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. Bitrate adaptation mechanisms, that drive video streaming, clash with caching mechanisms in ways that affect users' Quality of Experience (QoE). Cache performance also diminishes as consumers dynamically select content encoded at different bitrates. In this paper we use this evidence to draw a novel insight: in adaptive streaming over ICN, bitrates should be prioritized alongside popularity and hit rates. We build on this insight to propose RippleCache as a family of cache placement schemes that safeguard high-bitrate content at the edge and push low-bitrate content into the network core. Doing so reduces contention of cache resources, as well as congestion in the network. To validate RippleCache claims we construct two separate implementations. We design RippleClassic as a benchmark solution that optimizes content placement by maximizing a measure for ICNs shown to have high correlation with QoE. In addition, our lighter-weight RippleFinder is then re-designed with distributed execution for application in large-scale systems. RippleCache performance gains are reinforced by evaluations in NS-3 against state-of-the-art baseline approaches, using standard measures of QoE as defined by the DASH Industry Forum. Our results demonstrate that RippleClassic and RippleFinder deliver content that suffers less oscillation and rebuffering, all while achieving the highest levels of video quality; thus indicating overall improvements to QoE.

show abstract

Section: Related Workmentioning

confidence: 99%

Quality of Experience in ICN: Keep Your Low- Bitrate Close and High-Bitrate Closer

Oteafy

Fayed

et al. 2021

IEEE/ACM Trans. Networking

View full text Add to dashboard Cite

show abstract

“…For example, Jia et al [24] designed a control layer for optimal Interest forwarding and adaptive video caching based on the virtual queue of each bit rate. Kreuzberge et al [25] develop a cache-aware traffic-shaping policy in response to the unfair bandwidth sharing generated by rate-adaptive video streams. Liu et.al.…”

Section: Related Workmentioning

confidence: 99%

Quality of Experience from Cache Hierarchies: Keep your low-bitrate close, and high-bitrate closer

Li¹,

Oteafy²,

Fayed³

et al. 2019

Preprint

View full text Add to dashboard Cite

Recent studies into streaming media delivery suggest that performance gains from cache hierarchies such as Information-Centric Networks (ICNs) may be negated by Dynamic Adaptive Streaming (DAS), the de facto method for retrieving multimedia content. The bitrate adaptation mechanisms that drive video streaming clash with caching hierarchies in ways that affect users' Quality of Experience (QoE). Cache performance also diminishes as consumers dynamically select content encoded at different bitrates. In this paper we use the evidence to draw a novel insight: in a cache hierarchy for adaptive streaming content, bitrates should be prioritized over or alongside popularity and hit rates. We build on this insight to propose RippleCache as a family of cache placement schemes that safeguard high-bitrate content at the edge and push low-bitrate content into the network core. Doing so reduces contention of cache resources, as well as congestion in the network. To validate RippleCache claims we construct two separate implementations. We design RippleClassic as a benchmark solution that optimizes content placement by maximizing a measure for cache hierarchies shown to have high correlation with QoE. In addition, our lighter-weight RippleFinder is then re-designed with distributed execution for application in large-scale systems. RippleCache performance gains are reinforced by evaluations in NS-3 against state-of-the-art baseline approaches, using standard measures of QoE as defined by the DASH Industry Forum. Measurements show that RippleClassic and RippleFinder deliver content that suffers less oscillation and rebuffering, as well as the highest levels of video quality, indicating overall improvements to QoE.

show abstract

“…For this, several cache management schemes have been studied by recent research papers. For instance, [ 16 ] proposed a cache aware traffic shaping policy based on content popularity to assign the appropriate bit rate for video transmission. Recently, the authors in [ 17 ] proposed a hop-by-hop congestion control mechanism by defining the max/min threshold of Interest rate to prevent the network from congestion between the consumer and the routers.…”

Section: Related Workmentioning

confidence: 99%

A Smart Congestion Control Mechanism for the Green IoT Sensor-Enabled Information-Centric Networking

Sukjaimuk

Nguyễn

Sato

2018

Sensors

View full text Add to dashboard Cite

Information-Centric Networking (ICN) is a new Internet architecture design, which is considered as the global-scale Future Internet (FI) paradigm. Though ICN offers considerable benefits over the existing IP-based Internet architecture, its practical deployment in real life still has many challenges, especially in the case of high congestion and limited power in a sensor enabled-network for the Internet of Things (IoT) era. In this paper, we propose a smart congestion control mechanism to diminish the network congestion rate, reduce sensor power consumptions, and enhance the network performance of ICN at the same time to realize a complete green and efficient ICN-based sensor networking model. The proposed network system uses the chunk-by-chunk aggregated packets according to the content popularity to diminish the number of exchanged packets needed for data transmission. We also design the sensor power-based cache management strategy, and an adaptive Markov-based sensor scheduling policy with selective sensing algorithm to further maximize power savings for the sensors. The evaluation results using ndnSIM (a widely-used ICN simulator) show that the proposed model can provide higher network performance efficiency with lower energy consumption for the future Internet by achieving higher throughput with higher cache hit rate and lower Interest packet drop rate as we increase the number of IoT sensors in ICN.

show abstract

Modelling the impact of caching and popularity on concurrent adaptive multimedia streams in Information-Centric Networks

Cited by 11 publications

References 17 publications

Quality of Experience in ICN: Keep Your Low- Bitrate Close and High-Bitrate Closer

Quality of Experience in ICN: Keep Your Low- Bitrate Close and High-Bitrate Closer

Quality of Experience from Cache Hierarchies: Keep your low-bitrate close, and high-bitrate closer

A Smart Congestion Control Mechanism for the Green IoT Sensor-Enabled Information-Centric Networking

Contact Info

Product

Resources

About