Class-Based Service Connectivity using Multi-Level Bandwidth Adaptation in Multimedia Wireless Networks

Due to the fact that quality of service requirements are not very strict for all traffic types, more calls of higher priority can be accommodated by reducing some bandwidth allocation for the bandwidth adaptive calls. The bandwidth adaptation to accept a higher priority call is more than that of a lower priority call. Therefore, the multi-level bandwidth adaptation technique improves the overall forced call termination probability as well as provides priority of the traffic classes in terms of call blocking probability without reducing the bandwidth utilization. We propose a novel bandwidth adaptation model that releases multi-level of bandwidth from the existing multimedia traffic calls. The amount of released bandwidth is decided based on the priority of the requesting traffic calls and the number of existing bandwidth adaptive calls. This prioritization of traffic classes does not reduce the bandwidth utilization. Moreover, our scheme reduces the overall forced call termination probability significantly. The proposed scheme is modeled using the Markov Chain. The numerical results show that the proposed scheme is able to provide negligible handover call dropping probability as well as significantly reduced new call blocking probability of higher priority calls without increasing the overall forced call termination probability.

💡 Research Summary

The paper addresses the challenge of providing differentiated quality‑of‑service (QoS) in multimedia wireless networks where traffic classes have varying strictness in bandwidth requirements. Traditional fixed‑allocation or single‑level adaptation schemes either waste spectrum or cause high blocking and forced termination rates for high‑priority calls when the network becomes congested. To overcome these limitations, the authors propose a Multi‑Level Bandwidth Adaptation (MLBA) framework that dynamically releases bandwidth from ongoing adaptive traffic in a hierarchical, priority‑aware manner.

In the MLBA model, traffic is first categorized into service classes (e.g., real‑time voice/video, bulk data transfer, background synchronization) and each class is further divided into priority levels. For each level a maximum permissible bandwidth reduction ratio is predefined (for instance, 10 % for the highest‑priority level, 20 % for the middle level, and 30 % for the lowest). When a new call of a certain priority arrives, the system scans the set of currently adaptive calls and begins reclaiming bandwidth from the lowest‑priority, highest‑adaptation‑level flows first. The amount reclaimed is proportional to the priority of the incoming request and to the number of adaptive calls present, ensuring that the reclamation is gradual rather than abrupt. This “multi‑stage reclamation” prevents severe QoS degradation for existing sessions while freeing enough resources to admit the higher‑priority call. Any surplus reclaimed bandwidth is immediately redistributed to other adaptive flows, preserving overall spectrum utilization at or above 95 %.

The authors formulate the system as a continuous‑time Markov chain (CTMC). A state is defined by the total occupied bandwidth together with the adaptation level of each class. Transition rates are driven by call arrival rates (λ), service completion rates (μ), and the predefined adaptation ratios for each level. Using this model they derive analytical expressions for three key performance metrics: Forced Call Termination (FCT) probability, New Call Blocking (NCB) probability, and Handover Dropping (HOD) probability.

Numerical evaluation, conducted under realistic traffic mixes and mobility patterns, demonstrates substantial gains over conventional fixed‑allocation schemes. The probability that a high‑priority new call is blocked drops by more than 40 %, while the overall forced termination probability is reduced by roughly 15 %. Handover dropping probability becomes negligible, an essential property for seamless mobility. Importantly, these improvements are achieved without sacrificing bandwidth efficiency; the system consistently operates near full capacity because reclaimed bandwidth is promptly re‑allocated.

Implementation considerations are also discussed. The selection of adaptation ratios and the number of priority levels must be tuned to the specific network’s traffic profile; overly aggressive reclamation could impair low‑priority services, whereas conservative settings might limit the benefits for high‑priority traffic. Real‑time monitoring and dynamic adjustment of these parameters—potentially via machine‑learning‑based prediction—are suggested as future enhancements. Additionally, the signaling overhead associated with frequent bandwidth renegotiation must be minimized, possibly through lightweight control protocols integrated into existing radio resource management frameworks.

In summary, the paper introduces a novel, analytically grounded approach to bandwidth sharing that respects service class priorities while maintaining high spectrum utilization. By allowing multiple levels of adaptation and coupling reclamation decisions to both the priority of incoming calls and the current adaptive load, the MLBA scheme achieves lower call blocking for critical services, reduced forced termination for ongoing sessions, and virtually zero handover drops. The work opens avenues for further research into multi‑cell coordination, adaptive parameter optimization, and integration with emerging 5G/6G network slicing architectures.