Max-Min Throughput-Optimal Multicast Link Adaptation for Non-Identically Distributed Link Qualities

Abstract

We consider multicasting the same data from one base station (BS) to several user equipments (UE) over a radio interface. Transmission is slotted into transmit resource units (RU). The qualities of the transmission channels from the BS to the UEs differ between RUs and between UEs. The transmit rate can be chosen by the BS independently in each RU based on the per UE channel qualities, which are assumed to be known by the BS. For each UE we calculate the throughput as the mean information received per RU over all RUs within a scheduling unit. Here, the multicast throughput is defined as the minimum UE throughput. We present a scheme to optimize the rate selections for all RUs such that the minimum UE throughput is maximized, even in the general case that the channel qualities of UEs are non-identically distributed. The achievable gains are evaluated using radio network simulations with realistic channel models.

Dynamic Resource Allocation for Scalable Video Multirate Multicast Over Wireless Networks

“…Although this scheme is easy to implement, it is obviously inefficient, because all cell-centre subscribers having good channel qualities have to endure a bad video quality. To overcome this drawback, opportunistic multicast schemes [20]- [22] were developed for providing more aggressive MCS selection. Specifically, a threshold-based solution was proposed in [20] that dynamically selected a subset of subscribers to be served within each scheduling slot.…”

“…The authors of [21] have made a step forward by striking a trade-off between error resilience and transmission rate. In [22], rate selection was optimized by maximizing the minimum throughput in a multicast group, even when the channel qualities were nonidentically distributed. In general, maximizing the spectral efficiency by relying on opportunistic strategies fails to provide any fairness guarantee among subscribers [23], since some of the subscribers experiencing low channel quality may be denied service.…”

“…Hence, the second item at the right side of (22) only depends on the decision variables n g . According to the Lyapunov optimization framework [34], instead of directly minimizing the "drift-plus-penalty" (19), we can solve the time-averaged optimization problem by minimizing the "loose" bound (22), which is as follows:…”

Multicast Resource Allocation Enhanced by Channel State Feedbacks for Multiple Scalable Video Coding Streams in LTE Networks

“…The opportunistic strategies [17] follow the idea to dynamically change the MCSs (and, consequently, the portion of served users) within each scheduling frame, either by adopting threshold-based solutions [17] [18] [19] [20] or by optimizing a given objective cost function, such as spectral efficiency or throughput [21] [22]. For instance, author in [22] optimized the rate selections for all the system resources to maximize the throughput of the user with the worst quality, even in the general case that the channel qualities of terminals are nonidentically distributed.…”

“…For instance, author in [22] optimized the rate selections for all the system resources to maximize the throughput of the user with the worst quality, even in the general case that the channel qualities of terminals are nonidentically distributed. As proposed in [23] [24], opportunisticbased schemes can support multi-rate applications by exploiting Multiple Description Coding (MDC).…”