Toward Optimal Grouping and Resource Allocation for Multicast Streaming in LTE

Zuhra, Sadaf ul; Chaporkar, Prasanna; Karandikar, Abhay

doi:10.1109/tvt.2019.2945987

Cited by 13 publications

(7 citation statements)

References 41 publications

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“…Multicasting has been recognized as an effective means of catering to bandwidthhungry video transmissions [3] in cellular mobile networks. Resource allocation algorithms designed for multicast streaming have been shown to serve significantly more users while minimizing the impact of multicast streaming on other services [4,13]. Further improvements in the performance of multicast video streaming have been achieved by exploiting the inherent loss-tolerant nature of video streams [14].…”

Section: Related Literaturementioning

confidence: 99%

“…Using traditional one-to-one or unicast communications, such applications involves transmitting the same content separately to each user, thus consuming a large portion of the available bandwidth. Multicast transmissions are an efficient means of catering to such services by serving users that need the same content simultaneously [3,4]. Multi-connectivity allows users to receive content from multiple base stations simultaneously.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Connectivity for Multicast Video Streaming in Cellular Networks

Zuhra,

Chaporkar,

Karandikar

et al. 2024

Network

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The escalating demand for high-quality video streaming poses a major challenge for communication networks today. Catering to these bandwidth-hungry video streaming services places a huge burden on the limited spectral resources of communication networks, limiting the resources available for other services as well. Large volumes of video traffic can lead to severe network congestion, particularly during live streaming events, which require sending the same content to a large number of users simultaneously. For such applications, multicast transmission can effectively combat network congestion while meeting the demands of all the users by serving groups of users requesting the same content over shared spectral resources. Streaming services can further benefit from multi-connectivity, which allows users to receive content from multiple base stations simultaneously. Integrating multi-connectivity within multicast streaming can improve the system resource utilization while also providing seamless connectivity to multicast users. Toward this end, this work studied the impact of using multi-connectivity (MC) alongside wireless multicast for meeting the resource requirements of video streaming. Our findings show that MC substantially enhances the performance of multicast streaming, particularly benefiting cell-edge users who often experience poor channel conditions. We especially considered the number of users that can be simultaneously served by multi-connected multicast systems. It was observed that about 60% of the users that are left unserved under single-connectivity multicast are successfully served using the same resources by employing multi-connectivity in multicast transmissions. We prove that the optimal resource allocation problem for MC multicast is NP-hard. As a solution, we present a greedy approximation algorithm with an approximation factor of (1−1/e). Furthermore, we establish that no other polynomial-time algorithm can offer a superior approximation. To generate realistic video traffic patterns in our simulations, we made use of traces from actual videos. Our results clearly demonstrate that multi-connectivity leads to significant enhancements in the performance of multicast streaming.

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Section: Related Literaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

Zuhra,

Chaporkar,

Karandikar

et al. 2024

Network

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“…This massive influx of video traffic consumes a substantial fraction of the limited amount of spectrum available for use by cellular systems, leading to severe network congestion and degraded quality of service. For such services, multicast transmission provides an excellent solution [1,2] that can serve users over shared spectral resources while also improving the quality of service.…”

Section: Introductionmentioning

confidence: 99%

“…However, since B frames encode differential information with respect to the past and future I and P frames, their loss has the least impact on the quality of the video. Therefore, in this work, we assume that the base station uses lossless allocation policies [2] to allocate sufficient resources for the lossless transmission of I and P frames, and the lossy transmission proposed here impacts only the B frames of a video stream.…”

Section: Introductionmentioning

confidence: 99%

Optimal Resource Allocation for Loss-Tolerant Multicast Video Streaming

Zuhra

Besser

Chaporkar

et al. 2023

Entropy

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In video streaming applications, especially during live streaming events, video traffic can account for a significant portion of the network traffic and can lead to severe network congestion. For such applications, multicast provides an efficient means to deliver the same content to a large number of users simultaneously. However, in multicast, if the base station transmits content at rates higher than what can be decoded by users with the worst channels, these users will experience outages. This makes the multicast system’s performance dependent on the weakest users in the system. Interestingly, video streams can tolerate some packet loss without a significant degradation in the quality experienced by the users. This property can be leveraged to improve the multicast system’s performance by reducing the dependence of the multicast transmissions on the weakest users. In this work, we design a loss-tolerant video multicasting system that allows for some controlled packet loss while satisfying the quality requirements of the users. In particular, we solve the resource allocation problem in a multimedia broadcast multicast services (MBMS) system by transforming it into the problem of stabilizing a virtual queuing system. We propose two loss-optimal policies and demonstrate their effectiveness using numerical examples with realistic traffic patterns from real video streams. It is shown that the proposed policies are able to keep the loss encountered by every user below its tolerable loss. The proposed policies are also able to achieve a significantly lower peak SNR degradation than the existing schemes.

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“…This is especially useful for transmitting video streaming content like live telecasts of sports events, movie premiers, political events, news telecasts. Using multicast for such services can save considerable network resources and serve a large number of users in a limited bandwidth [3], [4].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Multi-Connectivity for Multicast Video Streaming

Zuhra¹,

Chaporkar²,

Karandikar³

et al. 2022

Preprint

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Multi-connectivity has emerged as a key enabler for providing seamless connectivity in cellular mobile networks. However, its potential for improving the quality of multicast transmissions has remained unexplored. In this paper, we investigate the use of multi-connectivity in wireless multicast streaming. Multi-connectivity can significantly improve the performance of multicast services. It especially benefits the cell edge users who often suffer from poor channel conditions. In this work, we assess the impact of multi-connectivity on the performance of multicast streaming. We propose procedures for establishing multi-connectivity in a multicast system and address the associated resource allocation problem. We prove that the optimal resource allocation problem is NP-hard. We propose a greedy approximation algorithm for this problem and prove that no other polynomial-time algorithm can provide a better approximation. Since video streaming is the primary use case under consideration here, we use traces from actual videos to generate realistic video traffic patterns in our simulations. Our simulation results clearly establish that multi-connectivity results in considerable performance improvement in multicast streaming.

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Toward Optimal Grouping and Resource Allocation for Multicast Streaming in LTE

Cited by 13 publications

References 41 publications

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

Multi-Connectivity for Multicast Video Streaming in Cellular Networks

Optimal Resource Allocation for Loss-Tolerant Multicast Video Streaming

Leveraging Multi-Connectivity for Multicast Video Streaming

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