Analysis of Bonded Cellular Live Video Broadcasting Systems Based on the OSI Model

Abstract—
Live video contribution over public IP networks has become a critical component of modern broadcasting.
Systems such as TVU One leverage bonded cellular connectivity and multipath transport protocols
to deliver low-latency, high-reliability video streams over heterogeneous networks.
This article presents a technical analysis of such systems using the OSI reference model,
with emphasis on transport-layer mechanisms, latency modeling, packet loss mitigation,
and cross-layer optimization.

I. Introduction

Traditional broadcast contribution relied on satellite links and dedicated fiber,
offering deterministic bandwidth at high cost and limited flexibility.
The emergence of bonded cellular live IP video broadcasting
represents a paradigm shift toward software-defined, network-adaptive media transport.

TVU One is representative of this class of systems,
combining real-time video encoding with multipath IP transmission
to achieve broadcast-grade reliability over best-effort networks.

II. System Architecture Overview

The considered system consists of three logical entities:

• Field Unit (Encoder & Multipath Sender)
• Heterogeneous IP Network (4G/5G, Wi-Fi, Ethernet)
• Receiver / Cloud Reassembly Platform

The architecture assumes instability at lower OSI layers
and compensates through intelligent transport and application-layer control.

III. OSI Model Mapping

A. Physical and Data Link Layers (Layers 1–2)

Let each available link i be characterized by:

Bandwidth: B_i
Packet loss probability: p_i
One-way latency: d_i

In bonded cellular systems, these parameters are time-varying and statistically independent.

B. Network Layer (Layer 3)

All links operate over IP (IPv4/IPv6) and are abstracted into a logical multipath tunnel.
Routing decisions are handled implicitly by the transport layer.

C. Transport Layer (Layer 4)

The core innovation lies in the multipath transport mechanism (e.g., IS+),
which distributes packets across N paths.

The aggregate available throughput is approximated as:

B_total = Σ_i=1..N B_i

However, effective throughput must account for packet loss and redundancy.
With Forward Error Correction (FEC) rate r:

B_effective = (1 − r) · B_total

IV. Packet Loss and Error Correction Model

Assuming independent losses per path, the probability that a packet is lost
on all paths is:

P_loss = Π_i=1..N p_i

By introducing FEC with recovery capability k packets per block,
the residual loss probability becomes:

P_residual ≈ max(0, P_loss − k)

This illustrates why multipath transmission dramatically reduces effective packet loss
compared to single-link streaming.

V. Latency Analysis

End-to-end latency (L) can be decomposed as:

L = L_enc + L_tx + L_buf + L_dec

Where:

• L_enc: encoder delay (GOP-dependent)
• L_tx: network transmission delay
• L_buf: de-jitter and reordering buffer
• L_dec: decoding delay

In multipath systems:

L_tx = max(d₁, d₂, …, d_N)

This constraint explains why intelligent path selection
and latency-aware packet scheduling are critical.

VI. Cross-Layer Optimization

Bonded cellular broadcast systems implement feedback loops between OSI layers.
Let Q(t) represent measured network quality at time t.
The encoder bitrate R(t) is dynamically adjusted:

R(t) = f(Q(t), B_effective, P_residual)

This adaptive control loop ensures system stability under rapidly changing conditions.

VII. Discussion

The analysis demonstrates that systems such as TVU One are not simple streaming devices,
but distributed real-time systems combining:

• Multipath networking
• Probabilistic loss mitigation
• Latency-constrained control loops

Such architectures are directly applicable to live sports broadcasting,
emergency response systems, and real-time IoT multimedia platforms.

VIII. Conclusion

This article presented an OSI-based technical analysis of bonded cellular
live IP video broadcasting systems.
Through multipath transport, forward error correction, and adaptive control,
these systems achieve broadcast-grade performance over best-effort networks.

The presented models provide a foundation for further academic research
in real-time media transport and cross-layer optimization.