As video streaming is becoming the most popular application of Internet mo-
bile, the design and the optimization of video communications over wireless
networks is attracting increasingly attention from both academia and indus-
try. The main challenges are to enhance the quality of service support, and to
dynamically adapt the transmitted video streams to the network condition.
The cross-layer methods, i.e., the exchange of information among different
layers of the system, is one of the key concepts to be exploited to achieve this
goals. In this thesis we propose novel cross-layer optimization frameworks
for scalable video coding (SVC) delivery and for HTTP adaptive streaming
(HAS) application over the downlink and the uplink of Long Term Evolution
(LTE) wireless networks. They jointly address optimized content-aware rate
adaptation and radio resource allocation (RRA) with the aim of maximiz-
ing the sum of the achievable rates while minimizing the quality difference
among multiple videos. For multi-user SVC delivery over downlink wireless
systems, where IP/TV is the most representative application, we decompose
the optimization problem and we propose the novel iterative local approxi-
mation algorithm to derive the optimal solution, by also presenting optimal
algorithms to solve the resulting two sub-problems. For multiple SVC de-
livery over uplink wireless systems, where healt-care services are the most
attractive and challenging application, we propose joint video adaptation
and aggregation directly performed at the application layer of the transmit-
ting equipment, which exploits the guaranteed bit-rate (GBR) provided by
the low-complexity sub-optimal RRA solutions proposed. Finally, we pro-
pose a quality-fair adaptive streaming solution to deliver fair video quality
to HAS clients in a LTE cell by adaptively selecting the prescribed (GBR)
of each user according to the video content in addition to the channel condi-
tion. Extensive numerical evaluations show the significant enhancements of
the proposed strategies with respect to other state-of-the-art frameworks
