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Title: Resource allocation for scalable video transmission over next-generation wireless networks
Author: Bocus, Mohammud Zubeir
ISNI:       0000 0004 2734 6933
Awarding Body: University of Bristol
Current Institution: University of Bristol
Date of Award: 2012
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Advancements in broadband wireless networks and video compression tech- nologies have led to a tremendous increase in the demand for wireless multimedia services over recent years. Popular wireless transmission techniques enabling en- hanced throughput include orthogonal frequency division multiplexing (OFDM) while the recent video coding standard, namely the H.264/ AYC, enables up to twice the compression efficiency to be attained relative to previous video com- pression techniques. Regardless of these developments, the highly dynamic and unpredictable nature of wireless channels, along with the requirements for main- taining the quality of service (Q08) and seamless video playback for all users, impose severe constraints on the design of wireless multimedia systems. A video coding technique that has been developed for such environments is the scalable video coding (8YC), which allows parts of the encoded bitstream to be discarded in response to a drop in the channel quality. However, state-of-the-art resource allocation techniques for SYC transmission over the wireless medium suffer from high computational complexity. Low-complexity, sub-optimal alternatives, on the other hand, are not always adequate. Given the sparse nature of spectrum resources, and the paradigm shift in spectrum access with the advent of cognitive radio systems, it is evident that sub-optimal algorithms having large optimality gaps are not desired. In fact, such approaches would be in contradiction to the definition of spectrum efficient, cognitive radio systems. In this thesis, resource allocation schemes for the transmission of H.264 SYC over wireless networks are investigated. In particular, OFDM systems are consid- ered, including OFDM-based cognitive radio networks. Cross-layer optimisation techniques for fine grain scalable (FGS) video sequences are analysed. Although the problem is initially non-convex and has non-polynomial-time (NP) complex- ity, low complexity techniques are derived that lead to solutions very close to the optimal. Resource allocation schemes for coarse grain scalable (CGS) and medium grain scalable (MGS) sequences over OFDM-based cognitive systems are also investigated. As opposed to FGS, CGS/MGS do not allow an encoded bitstream to be truncated at random bit location. Consequently, new methods are derived that focus on this particular type of video coding. The presence of multiple antennas at the cognitive transmitter and their effect on the aggregate visual quality of all secondary users are also discussed. Furthermore, a joint call admission control (CAC) and resource allocation for the transmission of CGS and MGS video sequences over orthogonal frequency division multiple access (OFDMA) are analysed. This scheme considers the sce- narios where the available channel resources are not enough to support the video data of all users. Finally, rate-adaptation techniques for scalable video transmission over wire- less networks are presented. Rate-adaptation refers to the methods by which the encoding parameters of the video coding are adapted in response to the chan- nel conditions. Interestingly, it is shown that under a given channel condition, increasing the granularity of a scalable sequence lead to diminishing returns in terms of the rate achieved. Moreover, the transmission of scalable sequences over cognitive radio networks where perfect channel knowledge is not available is investigated. The effect of the granularity of the bitstream on the interference observed by incumbent users is also presented. It is shown that the probability of exceeding the interference threshold can be significantly reduced by proper specification of the video encoding parameters.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available