Performance Evaluation of Scalable Multi-cell On-Demand Broadcast Protocols

As mobile data service becomes popular in today's mobile network, the data traffic burden irrevocably increases. LTE 4G, as the next-generation mobile technology, provides high data rates and improved spectral efficiency for data transmission. Currently in the mobile network, mobile data service solely relies on the point-to-point unicast transmission. In the ever-evolving 4G mobile network, mobile broadcast may serve as a supplemental means of pushing mobile data content from the data server to the mobile user devices. As part of the LTE 4G specifications, the mobile broadcast technology referred to as eMBMS is designed for supporting the mobile data service. From eMBMS, SFN broadcast transmission scheme allows data broadcasting to be synchronized in all cells of a defined core network area. LTE 4G also enables single-cell broadcast scheme in which data broadcasting is taking place independently in every cell. In this thesis, besides SFN or single-cell broadcast transmission, a hybrid broadcast transmission scheme in which SFN and single-cell broadcast transmission are used interchangeably in the same network based on the network conditions is proposed. For on-demand data service, the pull-based scheduling protocols from previous work are originally designed to work in a single-cell case scenario. With slight modifications, the batching/cbd protocol can be adapted for multi-cell data service. A new combined scheduling protocol, that is cyclic/cd,fft protocol, is devised as the second candidate for multi-cell data transmission scheduling. Based on the three broadcast transmission schemes and the two broadcast scheduling protocols, six mobile broadcast protocols are proposed. The mobile broadcast models, which correspond to the six mobile broadcast protocols, are evaluated by analysis and simulation experiment. By analysis, the cost equations are derived for calculating average server bandwidth, average client delay and maximum client delay of the mobile broadcast models. In the experiment, the input parameters of broadcast test models are assessed one at a time. The experimental results show that the hybrid broadcast transmission together with cyclic/cd,fft protocol would provide the best server bandwidth performance and the SFN broadcast transmission together with batching/cbd protocol provides the best average delay performance

Multicast Protocols for Scalable On-Demand Download

Previous scalable protocols for downloading large, popular files from a single server include batching and cyclic multicast. With batching, clients wait to begin receiving a requested file until the beginning of its next multicast transmission, which collectively serves all of the waiting clients that have accumulated up to that point. With cyclic multicast, the file data is cyclically transmitted on a multicast channel. Clients can begin listening to the channel at an arbitrary point in time, and continue listening until all of the file data has been received. This paper first develops lower bounds on the average and maximum client delay for completely downloading a file, as functions of the average server bandwidth used to serve requests for that file, for systems with homogeneous clients. The results show that neither cyclic multicast nor batching consistently yields performance close to optimal. New hybrid download protocols are proposed that achieve within 15 % of the optimal maximum delay and 20 % of the optimal average delay in homogeneous systems. For heterogeneous systems in which clients have widely-varying achievable reception rates, an additional design question concerns the use of high rate transmissions, which can decrease delay for clients that can receive at such rates, in addition to low rate transmissions that can be received by all clients. A new scalable download protocol for such systems is proposed, and its performance is compared to that of alternative protocols as well as to new lower bounds on maximum client delay. The new protocol achieves within 25 % of the optimal maximum client delay in all scenarios considered