We are currently witnessing a growing interest of network operators to migrate their existing 2G/3G networks to 4G technologies such as long-term evolution (LTE) to enhance the user experience and service opportunities in terms of providing multi-megabit bandwidth, more efficient use of radio networks, latency reduction, and improved mobility. Along with this, there is a strong deployment of packet data networks such as those based on IEEE 802.11 and 802.16 standards. Mobile devices are having increased capabilities to access many of these wireless networks types at the same time. Reinforcing quality of service (QoS) in 4G wireless networks will be a major challenge because of varying bit rates, channel characteristics, bandwidth allocation and global roaming support among heterogeneous wireless networks. As a mobile user moves across access networks, to the issue of mapping resource reservations between different networks to maintain QoS behavior becomes crucial. To support global roaming and interoperability across heterogeneous wireless networks, it is important for wireless network operators to negotiate service level agreement (SLA) contracts relevant to the QoS requirements. Wireless IP traffic modeling (in terms of providing assured QoS) is still immature because the majority of the existing work is merely based on the characterization of wireless IP traffic without investigating the behavior of queueing systems for such traffic. To overcome such limitations, we investigate SLA parameter negotiation among heterogeneous wireless network operators by focusing on traffic engineering and QoS together for 4G wireless networks. We present a novel mechanism that achieves service continuity through SLA parameter negotiation by using a translation matrix, which maps QoS parameters between different access networks. The SLA matrix composition is modeled analytically based on the G/M/1 queueing system. We evaluate the model using two different scheduling schemes and we derive closed form expressions for different QoS parameters for performance metrics such as packet delay and packet loss rate. We also develop a discrete event simulator and conduct a series of simulation experiments in order to understand the QoS behavior of corresponding traffic classes.